Metadata schema for simulation studies using The Virtual Brain neuroinformatics platform or other dynamic network models of large-scale brain activity.
Coarse classification of this model (mean_field, neural_mass, phase_oscillator, phenomenological, spiking, generic, field). Used for filtering in Dynamics.list_db(model_type=...).
Add the center coordinate of this entity.
Common coordinate space (e.g. FSLMNI152, MNI152NLin2009cAsym). Reference by name; the CommonCoordinateSpace must be defined in tvbo/database/coordinate_spaces/ (or future equivalent location).
Add all anatomical parent structures for this entity as defined within the corresponding brain atlas.
Add the parcellation terminology version used for this brain atlas version.
Affect function: what happens when the event triggers. Can modify state variables and/or parameters. For stimulus type, this is the stimulus equation.
Affect on downcrossing (continuous events only). If not specified, uses the same affect for both crossings.
Iterative parameter tuning algorithms (FIC, EIB, etc.)
Dimension along which the distribution is sampled. 'space' = per-node (default), 'time' = per-timestep (stochastic input).
Parameter (CURIE into the per-model parameter namespace) along which the bifurcation is observed.
bifurcation parameter
Object is a Bifurcation class (e.g. tvbo:Hopf). Encoded as a class so DL reasoners can use the subClassOf hierarchy on the Bifurcation tree.
bifurcation type
Constraints on parameter values after update
Child branches to continue from detected bifurcation points (PO from Hopf, fold continuation, etc.).
Arguments passed when calling the instance (__call__). Usually the input data from simulation result. Example: result (simulation output array).
Class instantiation and call (alternative to callable/function). Use for external library classes that need __init__ then __call__. Example: Bold monitor from tvboptim.
Direct class reference (alternative to pipeline). Use for external library classes like tvboptim.Bold, custom monitors, or any callable class. The class is instantiated with constructor_args and called with call_args. Example: {name: Bold, module: tvboptim.observations.tvb_monitors.bold, constructor_args: [{name: period, value: 1000.0}]}
Conditional logic for piecewise equations.
Arguments passed to __init__ when instantiating the class. Example: period=1000.0, downsample_period=4.0 for Bold monitor.
Override solver settings for this branch. Uses the same Continuation type — only explicitly set attributes override the parent's values.
Numerical continuation and bifurcation analysis specifications. Each entry defines a continuation experiment (equilibrium branch, codim-2 curve, periodic orbit family, etc.). # Either reference a full reusable environment (preferred) or, for
Node coordinates (x,y,z) in the given coordinate space.
Mathematical function defining the coupling
Multi-subject dataset for workflow rendering. When set, render_workflow() uses dataset.subjects/sessions to generate per-subject parallel jobs.
Observations derived from combining other observations. Computed after all regular observations are available. Examples: fc_corr (from fc, fc_target), rmse, etc.
Unit for distances/lengths in the network
List of directed edges with coupling references (optional, for explicit edge definition)
Primary engine (must appear in environment.requirements; migration target replacing deprecated 'software').
Execution environment (collection of requirements).
executable on
Range is a reified BifurcationEvent that records the bifurcation type, the locus parameter, and (optionally) the critical value.
exhibits bifurcation
Object is a Regime class (open-world: model may admit additional regimes for parameter values not yet catalogued).
exhibits regime
Parameter exploration/grid search specifications
Primary field being solved for (deprecated; use state_variables).
Parameters from previous stages to freeze (keep at optimized value but not update)
Graph generator specification. When set, overrides explicit edges/nodes for graph construction. The type field is a free string; StandardGraphType lists well-known types that get automatic code generation across backends.
has attractor
has model feature
Intrinsic stochasticity of the model definition itself. A model whose equations are deterministic but is integrated with additive noise via the Integrator is still tvbo:Deterministic here.
has stochasticity character
Upper bound or stopping value. Can be a number or argument name.
Type of imaging modality (BOLD, EEG, MEG, etc.)
Include update rules from other algorithms with optional argument overrides. Unlike depends_on (sequential), includes means combined execution. Example: includes: [{algorithm: fic, arguments: [{name: eta, value: 0.1}]}]
References to state variables from connected (source) nodes. Auto-populated from state_variables with coupling_variable=true when omitted. Used by name in pre_expression.
Analytic initial condition for the field.
How to obtain the initial equilibrium. Default: time integration to steady state.
For hierarchical coupling: inner coupling applied at regional level
Optional scalar or vector intensity parameter for noise.
Linear solver for the Newton bordered system. E.g. COPBLS (collocation), MatrixBLS (shooting/poincaré).
Lower bound or starting value. Can be a number or argument name.
Callable that loads the data (e.g., load_functional_connectivity)
References to state variables from the local (target) node. Used by name in pre_expression.
Loss function call. Uses FunctionCall to either: 1. Reference existing function: function: rmse 2. Inline callable: callable: {module: ..., name: ...} Arguments specify inputs (simulated_fc, empirical_fc, etc.)
Metric equation for matching (e.g., correlation, rmse)
Imaging modality or simulation output type.
Anatomical region(s) the Dynamics model is intended to represent. Range: UBERON terms.
models anatomical region
Cell type(s) whose collective dynamics the model represents. Range: CL terms.
models cell type
Neurotransmitter system(s) whose synaptic dynamics the model represents. Range: GO biological-process terms.
models neurotransmitter system
List of nodes with individual dynamics (optional, for heterogeneous networks)
Observable to compute at each point. Use function: obs_name for simple observation, or function: func_name + arguments for FunctionCall.
ODE solver for flow-based methods (shooting, poincaré). Specifies algorithm (e.g. Vern9, Rodas5) and tolerances. Not needed for collocation or trapezoid.
Output transformation equation (if equation-based)
Dimension to aggregate over (e.g., node, time, state)
Ordered sequence of Functions. Each step has a unique `name` (used to key step outputs) and transforms input -> output. List form preserves execution order.
Spatial coordinates (x, y, z) of the node
Post-processing expression applied after coupling
Pre-processing expression applied before coupling
Optional preprocessing to apply after loading
provides capability
For hierarchical coupling: vertex-to-region mapping for aggregation
For hierarchical coupling: region-to-region connectivity with weights and delays
Observables required by this update rule
Software environment used to generate this data.
Reference to the SimulationExperiment that generated this TimeSeries.
One or more observations to derive from. For transformations (e.g., fc from bold), use single source. For comparisons (e.g., fc_corr from fc and fc_target), use multiple sources. Order may matter for asymmetric operations.
Ordered list of exploration axes spanning the search space. Each axis references an existing Parameter (by dotted name, e.g. "ReducedWongWang.w" or "FastLinearCoupling.G") and supplies the Range to sweep. No new Parameter is created here.
Ordered list of optimization stages. Stages run sequentially. Stage n+1 starts from optimized values of stage n. When defined, inherited single-stage fields are ignored.
Per-node initial state variable values, keyed by state variable name.
Step size. Can be: number, argument name, or expression.
supports task
Reference to empirical data observation for matching objectives
The parameter to update (e.g., J_i, wLRE)
State variable for activity targets (e.g., S_e)
State variables this noise applies to; if omitted, applies globally.
Optional anisotropic tensor (e.g., diffusion).
Time unit for the integration / simulation. Determines the physical time meaning of one model time-step.
Ordered list of transforms applied to edge property matrices. Each Function's name identifies the target edge property (e.g. 'weight', 'length'). Supports equation-based (symbolic) or callable-based (software) transforms. Multiple transforms on the same target are applied sequentially.
How parameters are updated each iteration. When using 'includes', update_rules are inherited from included algorithms.
Previous stage to initialize from. Final values from that stage become initial values for this stage.
Observed improvement relative to baseline based on a defined score.
List of physical contacts along the electrode
3D coordinate of the contact center in the defined coordinate space
Metadata about the E-field result for this setting
If this score is a subscore of a broader composite
All DBS protocols assigned to this subject.
The scale this score belongs to, if applicable
Anatomical target structure from a brain atlas
The planned trajectory for electrode implantation
Development status (repostatus.org aligned).
Package ecosystem(s) through which the software is distributed.
Tools this tool can exchange data with or delegate to.
Reference to the software package identity.
Spatial/organizational scales the tool operates at.
Primary function(s) in a simulation workflow.
Data collection sessions for this subject. Each session can have its own network, empirical data, and conditions.
Add the digital identifier of this brain atlas or coordinate space version.
Enter the label used for looking up this entity in the parcellation terminology.
Add the original label of this entity as defined in the parcellation terminology.
Whether the system is autonomous (equations do not depend explicitly on time t). Non-autonomous systems have explicit time dependence, e.g. f*cos(omega*t).
Batch size for vectorized operations (None = auto)
Whether this state variable is transmitted to connected nodes through the coupling function. In TVB terms, this determines the cvar indices (state variables extracted from history and fed into the coupling function). The coupling function may override this via its incoming_states attribute.
critical value
Polynomial degree per mesh interval for collocation. The m in PeriodicOrbitOCollProblem(N, m).
Initial parameter offset from the bifurcation point.
Initial value for the first time derivative, used when equation_order > 1. For a second-order ODE d²x/dt² = f, this sets dx/dt(0). Required by ModelingToolkit.jl to fully specify higher-order initial value problems.
Bifurcation detection level. 0 = off, 1 = eigenvalues only, 2 = detect, 3 = locate precisely.
Dimensionality of the coupling input (number of coupled values)
Edge length / tract distance (explicit edges, mm)
Intermediate downsampling period (ms). For BOLD: typically matches dt.
Element/node index this range applies to. Used in element_domains to explicitly link a domain to a specific element of a heterogeneous parameter (e.g., element: 0 for node 0). Required when used in element_domains to avoid ambiguous positional indexing.
Order of the time derivative on the LHS. Default 1 means dx/dt = rhs (first-order ODE). Order 2 means d²x/dt² = rhs (second-order ODE), etc. Higher-order ODEs are automatically lowered to coupled first-order systems by backends like ModelingToolkit.jl via mtkcompile.
Whether to find a fixed point (steady state) before time integration. Used as initial condition for ODEProblem. Maps to NLsolve.fixpoint! in ND.jl or similar in other backends.
Timestamp when this TimeSeries was generated.
External ontology IRIs (typically GO, ChEBI, UBERON, CL, MeSH) that this entity is a surrogate / abstraction / model of. Replaces the legacy OWL pattern `tvbo:surrogate_of` by carrying the link inline with the YAML data instance. Multiple IRIs allowed: a single parameter may abstract several biological processes (e.g. a synaptic conductance grounding both GO:0060079 (excitatory PSP) and GO:0007268 (chemical synaptic transmission)).
has conserved quantity
has timescale separation
Optional stable IRI (or compact URI) for this entity in an external ontology or knowledge base. Used to load metadata from an external source; not required when the entity is fully self-contained (equations, parameters, etc. defined in the file itself).
Linear warmup of learning rate from 0 to learning_rate over n_iterations. eta_effective = eta * (i+1) / n_iterations
Maximum bisection steps for bifurcation point localization.
Number of mesh intervals (time slices) for collocation or trapezoid methods. Collocation: N in PeriodicOrbitOCollProblem(N, m). Trapezoid: M in PeriodicOrbitTrapProblem(M=...).
Number of points (alternative to step for grid exploration).
Number of eigenvalue sign inversions to flag a bifurcation. Must be even. Higher = fewer false positives.
Number of shooting sections for shooting or Poincaré methods.
Number of CPU threads per worker (-1 = auto-detect)
Number of independent trials per grid point. Each trial uses a different noise seed. Used for averaging stochastic simulations (e.g., VEP = average of 20 trials).
Number of parallel workers (maps to pmap devices in JAX, processes in multiprocessing)
Number of eigenvalues to compute. Must be >= number of state variables for reliable Hopf detection.
Maximum Newton corrector iterations per step.
Absolute tolerance for Newton corrector convergence.
Number of elements (triangles, quads, tetrahedra, etc.).
Number of nodes in the network (derived from nodes if not set)
Number of subjects in the tractography dataset
Sampling period for monitors (ms). For BOLD: TR in ms.
phase-space dimension
Whether to include this element in simulation output files. Applicable to state variables (default true), derived variables (default false), and network nodes (default true). Set false to suppress recording.
Time between samples (inverse of sampling_rate).
Duration of each simulation step (e.g., one BOLD TR)
Number of samples to skip at the start (transient removal). For FC: typically 10-20 TRs.
Whether the coupling uses sparse representations
Number of samples from the end to use. Takes the last N samples before aggregation. E.g., tail_samples: 500 means use data[-500:].
Target node ID (set for explicit edges, absent for template edges)
Tolerance on real part of eigenvalue for stability boundary.
Predetermined trigger times for preset_time events. The solver will step exactly to these times.
Array mapping each vertex index to its parent region index. Can use dataLocation instead for large arrays.
Variable of interest index (which state variable to monitor). Default: 0.
Whether to apply learning rate warmup to this update rule. When true, the learning rate (eta) is scaled by (i+1)/n_iterations.
Matrix entries carry weights (not just 0/1)
Number of samples for windowed aggregation
Absolute value of the score at the time of assessment
Preoperative baseline value of the score
Percent change compared to preoperative baseline (positive = improvement)
Threshold value applied to the E-field simulation
Timepoint of assessment in days or months after implantation
Date the software was first released (YYYY-MM-DD).
Date of the most recent release or significant update.
URIs that unambiguously identify this tool (Wikidata, bio.tools, RRID, SciCrunch).
Millimole per cubic metre (mmol/m³ ≈ mM)
A schema for representing a version of a brain atlas.
A schema for representing a version of a common coordinate space.
A schema for representing a parcellation entity, which is an anatomical location or study target.
A schema for representing a parcellation terminology, which consists of parcellation entities.
Whole-brain or large-scale network of regions.
Mean-field reductions of spiking networks.
Bridging multiple spatial/temporal scales.
Model description language or data standard.
Microcircuit / local network of neurons.
Multi-compartment morphologically detailed models.
NEURON's high-level interpreted language.
Phase-reduced or Kuramoto-type oscillator models.
Phenomenological population-rate / neural-mass models.
Population-level neural mass or mean-field model.
Conductance-based / Hodgkin-Huxley-type models.
Database or repository of published models.
Synaptic plasticity (STDP, homeostatic, etc.).
Spiking neuron models (LIF, AdEx, Izhikevich, etc.).
Stochastic or deterministic reaction-diffusion.
Dynamical systems bifurcation / continuation analysis.
Numerical continuation / bifurcation analysis.
Ion channel / sub-cellular molecular dynamics.
Visualization or graphical user interface.
Post-processing, signal analysis, or statistics.
Additive noise
Specifies how to aggregate values across a dimension. Used for loss functions to define per-element loss with reduction.
A complete specification of an iterative parameter tuning algorithm. Combines update rules, objectives, observations, and hyperparameters.
Reference to an included algorithm with optional argument overrides. Allows combining algorithms with different hyperparameter values.
A function argument with explicit value specification. Value can be: literal (number/string), reference to input (input.key), or cross-observation reference (observation_name.output_key).
Attractor
A code-generation or simulation backend (JAX, TVB, PyRates, tvboptim, NetworkDynamics.jl, BifurcationKit.jl).
Backend
A feature a backend either provides or lacks (autodiff, JIT, GPU, vectorised RNG, delay buffer, stochastic solver, stiff solver, continuation solver, code generation, etc.). Used to gate task support.
Backend capability
BIDS filename entities (BEP017-aligned) for provenance and data discovery. Reusable on Network, BrainAtlas, Tractogram, or any dataset with BIDS-conformant naming.
Bifurcation
Reified record of a bifurcation in a specific model along a specific parameter.
Bifurcation event
Bogdanov-Takens
A series whose values represent latitude
Specification for switching from a detected bifurcation point to a new branch (periodic orbits from Hopf, fold continuation, etc.). Each BranchSwitch says: "from which special point on the parent branch, continue what kind of object, with what settings." Override parent solver settings via the inline continuation field — only explicitly set attributes take effect; everything else is inherited from the parent Continuation.
Reference to a class that can be instantiated and called. Used for external library classes (e.g., tvboptim.Bold, custom monitors). The class is instantiated with constructor_args, then called with call_args. Generalizable pattern: works for tvboptim, TVB, or any Python class.
Coloured noise
A single condition and its corresponding equation segment.
Complete specification of a numerical continuation / bifurcation analysis. All universal solver settings live directly here. Toolkit-specific string options go in the options slot. When used inside a BranchSwitch, only explicitly set attributes override the parent's values.
Predictor-corrector algorithm for numerical continuation.
Specification for loading external/empirical data.
Observation derived from one or more other observations. Examples: - fc (from bold) - single source transformation - fc_corr (from fc and fc_target) - multi-source comparison Unlike regular Observations, these don't generate monitor classes but are computed from existing observation values.
Development status of the software. Based on repostatus.org categories.
Dimensions along which operations can be applied
Discretization method for boundary value problems in continuation (periodic orbits, connecting orbits, quasi-periodic tori). Specifies the method; method-specific numerics go in parameters.
A probability distribution for sampling parameters or initial conditions. Standard distributions (Uniform, Gaussian) are specified by name and domain/parameters. Custom distributions use a Function for the PDF/sampling rule. Default name is Uniform when only domain is given.
Top-level grouping for qualitative phase-space properties.
Dynamical property
Dynamical regime
Successor class replacing deprecated NeuralMassModel.
Package ecosystem or registry the software is distributed through.
An edge in a network. Two modes: explicit (source+target set, scalar parameters in YAML) or template (no source/target, N×N matrix measure in HDF5). Both coexist in the same edges list.
A discrete or continuous event that modifies the system during simulation. Generalizes Stimulus: can represent external inputs (stimulus type), threshold-triggered state changes (continuous/discrete type), or time-scheduled interventions (preset_time type). Attaches to components (nodes/edges) or to the experiment level.
Configuration for computational execution (parallelization, precision, hardware).
Parameter space exploration (grid search, sweep).
One axis of a parameter exploration grid. Points to an existing Parameter (by dotted reference, e.g. "ReducedWongWang.w" or "FastLinearCoupling.G") and supplies the sweep specification (domain, explored_values, or per-element overrides). No new Parameter is created.
Fold of limit cycles
One degree of freedom in an OptimizationStage. References an existing Parameter by dotted scope (e.g. "ReducedWongWang.w" or "FastLinearCoupling.G") and supplies optimization-specific metadata (heterogeneous, shape, bounds, initial value). No new Parameter is created here.
A function with explicit input -> transformation -> output flow. Can be equation-based (symbolic) or software-based (callable). In a pipeline, functions are chained: output of one becomes input of next.
Invocation of a function in a pipeline. Can reference a defined Function by name, OR inline a callable directly for external library functions, OR inline an equation, OR use class_call for class instantiation. Mirrors Function attributes so pipeline steps can be self-contained. The `name` is an optional step label (used in pipelines for keyed access to step outputs); it is NOT a global identifier (singleton uses like `loss`, `observable` may omit it).
Backend-agnostic graph generator specification. Captures the mathematical family (type) and its parameters so that each backend can emit the correct constructor call (Graphs.jl, NetworkX, etc.). The number of nodes is always taken from Network.number_of_nodes.
How to obtain the starting equilibrium or periodic orbit for continuation. Most robust: time-integrate to steady state.
Strategy for obtaining the starting equilibrium or periodic orbit.
Fixed-step or adaptive ODE integrator with TVB-specific extensions (noise, transient time, etc.). Inherits abs_tol, rel_tol from Solver. Overrides method default to 'euler'.
A loss function for optimization with optional aggregation. Extends Function with aggregation specification for per-element losses.
Intrinsic structural trait of a Dynamics model (e.g. sigmoid activation, fast-slow decomposition, E/I subpopulations). Distinct from BackendCapability: a feature is what the model has, a capability is what a backend provides.
Model feature
Computational paradigm or modeling approach supported by the tool.
Coarse classification of a Dynamics model by its mathematical/biological origin. Used for filtering and display in list_db().
Multiplicative noise
Network specification with nodes, edges, and reusable coupling configurations. Supports both explicit node/edge representation and matrix-based connectivity (Connectome compatibility).
A node in a network with its own dynamics and properties
Numerical discretization method for boundary value problems (periodic orbits, connecting orbits, quasi-periodic tori).
Unified class for all observation/measurement specifications. Covers monitors (BOLD, EEG), tuning observables, and derived quantities. Pipeline is a sequence of Functions with input -> output flow.
Configuration for parameter optimization. Inherits single-stage fields from OptimizationStage. For multi-stage workflows, use 'stages' (ignores inherited single-stage fields). Loss equation references observations directly by name.
A single stage in a multi-stage optimization workflow. Stages run sequentially, with each stage potentially using different parameters, shapes, learning rates, and algorithms.
A toolkit-specific key-value option (string name + string value). Used for backend settings that are not universal numeric parameters (e.g., solver name, tangent method, jacobian type).
Partial differential equation problem definition.
Period doubling
Physical dimension categories for LEMS and dimensional analysis. Each dimension decomposes into SI base dimensions (M, L, T, I, K, N).
Pitchfork
Programming languages relevant to computational neuroscience tools. Mapped to Wikidata identifiers.
W3C PROV-O aligned provenance. Reusable on any entity (Network, TimeSeries, Dynamics, etc.).
Specifies a range for array generation, parameter bounds, or grid exploration.
Operations for reducing/aggregating values across dimensions
Maps vertices to parent regions for hierarchical/aggregated coupling
Dimension along which a distribution is sampled.
A scalar value: float, integer, string, or boolean. Used for slots that accept literals of any primitive type, such as argument values which may be numbers, strings, references, or booleans.
Spatial / organizational scale at which a tool operates. Multi-valued: a tool can span multiple scales. Mapped to SIO and Wikidata where possible.
A computational task: forward integration in some formalism, parameter exploration, gradient-based optimisation, numerical continuation, or bifurcation analysis.
Simulation task
Lightweight specification of a numerical ODE solver / integrator. Covers adaptive solvers (Vern9, Rodas5, Tsit5, etc.) used in shooting methods, initial-state integration, and other contexts where only the algorithm and tolerances matter.
A set of permissible types for specimens used in brain atlas creation.
Well-known graph generator families with automatic backend mapping. The type field on GraphGenerator is a free string; this enum lists common types that get automatic code generation for Julia (Graphs.jl) and Python (NetworkX).
A named state variable value for per-node initialization.
Stochasticity character of the model definition itself, independent of any optional integrator-level noise injection.
Stochasticity character
Subcritical Hopf
Time series data from simulations or measurements. Supports BIDS-compatible export for computational modeling (BEP034).
Supports BIDS BEP034 computational modeling extension.
Use to_bids() method in Python class to export as BIDS dataset.
Primary function of the tool in a simulation workflow.
Reference to tractography/diffusion MRI data used to derive structural connectivity
Transcritical
Defines what the tuning algorithm optimizes for. Can be an activity target (FIC) or a connectivity target (EIB).
Physical units of measurement for model parameters, state variables, and integration settings. Uses conventional abbreviations as values, mapped to the QUDT ontology (http://qudt.org/vocab/unit/) with UO cross-references where available.
Defines how a parameter is updated based on observables. Represents iterative learning rules like FIC or EIB updates. Functions from experiment.functions are available in the equation.
Natural parameter continuation. Simple parameter stepping, no arc-length constraint.
Pseudo-arclength continuation (default). Uses weighted dot product constraint.
Actively developed with regular releases.
Minimal or no implementation; ideas / prototypes.
No longer actively developed; may still work.
Project has been moved to a different location.
Development paused; may resume in future.
Work in progress; not yet feature-complete.
Batch dimension (for parallel processing)
Network node dimension (general graph term)
Spatial/regional dimension (alias for node in brain networks)
Triggered when condition function crosses zero (root-finding). Maps to ContinuousCallback / ContinuousComponentCallback.
Triggered when condition function returns true (checked at each step). Maps to DiscreteCallback / DiscreteComponentCallback.
Triggered at predetermined time points. Maps to PresetTimeCallback / PresetTimeComponentCallback.
Continuous time-dependent input signal (e.g., external current). Legacy Stimulus behavior.
Start from a point on a previously computed branch.
Use the model's default initial values directly.
Use Newton's method to find the nearest fixed point.
Integrate the ODE forward until convergence (robust, default).
Dynamic mean-field approximation (e.g., Deco et al.).
General-purpose, not specific to neuroscience.
Declarative model specification language.
Spatially distributed neural-field models described by integro- differential or PDE formulations.
Generic / normal-form dynamical systems not specific to neural modelling (e.g. Generic2dOscillator, GenericLinear).
Mathematically derived mean-field models obtained by exact reduction of spiking networks (Ott-Antonsen ansatz, Lorentzian heterogeneity, etc.). Examples: MontbrioPazoRoxin, CoombesByrne, ReducedWongWang, ZerlautAdaptationFirstOrder.
Phenomenological population-rate / neural-mass models that describe synaptic and firing-rate dynamics without an explicit derivation from single-neuron statistics. Examples: JansenRit, WilsonCowan, LarterBreakspear, TsodyksMarkram.
Phase-reduced or Kuramoto-type oscillator models. Examples: Kuramoto, SupHopf.
Empirical / phenomenological models that capture macroscopic dynamics without direct biophysical derivation. Examples: Epileptor2D, Epileptor5D.
Single-neuron or conductance-based spiking models (HH, AdEx, LIF, Izhikevich, etc.). These can be used as nodes in a network alongside mean-field models.
collocation (NumericalDiscretizationMethod)
poincare (NumericalDiscretizationMethod)
shooting (NumericalDiscretizationMethod)
trapezoid (NumericalDiscretizationMethod)
No reduction (return per-element values)
Sample once per node (heterogeneous parameter or spatially varying IC).
Resample every integration timestep (stochastic time-varying input).
Compressed Sparse Row (data, indices, indptr)
Barabasi-Albert preferential attachment (params: k)
Watts-Strogatz small-world (params: k, p)
Continuous-time dynamics (e.g., ODE/SDE).
Discrete-time dynamics (e.g., maps, iterated updates).
Optimized execution backend for another simulator.
Orchestration, model-building, or pipeline tool.
Siemens per square centimetre (conductance density)
Siemens per square metre (conductance density, SI)
Millisiemens per square centimetre (conductance density)
Mole per metre per ampere per second (concentration-current coupling)
Per-unit (dimensionless power-systems convention)
Microampere per square centimetre (current density)
Microfarad per square centimetre (specific capacitance)
Relative improvement on a defined clinical score.
A clinical assessment inventory or structured scale composed of multiple scores or items.
Metadata about a clinical score or scale.
Collection of data related to a specific DBS study.
A protocol describing DBS therapy, potentially bilateral or multi-lead.
Simulated electric field from DBS modeling.
Implanted DBS electrode and contact geometry.
A software tool for computational neuroscience simulation, analysis, or model specification. Extends SoftwarePackage with neuroscience-specific controlled vocabularies for scale, paradigm, role, and interoperability. Aligned with CodeMeta v3 and DOAP.
A reproducible software environment aggregating one or more SoftwareRequirement entries. Used by SimulationExperiment to specify the execution context.
An environment aggregates one or more SoftwareRequirement entries.
Use SimulationExperiment.environment to reference a reusable environment.
Identity and metadata for a software package, aligned with schema.org/SoftwareApplication and CodeMeta v3.
An individual software requirement binding a package to a version constraint and a role within an environment.
Represents an individual requirement (package/module/library).
Use 'version_spec' instead of 'version' for semantic clarity.
A collection of subjects for a multi-subject study. Provides the subject/session structure needed for workflow rendering. Optionally backed by a BIDS directory layout.
A data collection session for a subject. Corresponds to a BIDS 'ses-' entity. Sessions capture longitudinal timepoints (baseline, follow-up), different experimental conditions, or repeated measures.
A participant in a study. Each subject typically has their own brain network (connectome) and empirical recordings. Corresponds to a BIDS 'sub-' entity.
Automatic differentiation
Detection and classification of bifurcations along a continuation branch.
Bifurcation analysis
BifurcationKit.jl
Built-in model library
Adaptive linear-nonlinear (aLN) mean-field model of a network of adaptive exponential integrate-and-fire (AdEx) neurons, comprising one excitatory (E) and one inhibitory (I) population. Originally developed by Augustin, Ladenbauer, Liebe & Obermayer (2017) and used as the whole-brain mean-field in neurolib by Cakan, Jajcay & Obermayer (2021). The legacy tvb-o.owl ontology referred to this model by the short name ``Cortical`` (suffix ``_CO``); ``iri`` is preserved for backward ontology mapping while ``name`` reflects the canonical attribution. Defaults are taken verbatim from ``neurolib/models/aln/loadDefaultParams.py``. Note that the full aLN dynamics require a precomputed transfer-function lookup table (``aln-precalc/quantities_cascade.h5``) that maps the filtered mean input ``mu`` and the membrane-potential standard deviation ``sigma`` to a steady-state firing rate. The state-variable equations below are the structural filtered-mean-input scaffold; the nonlinear transfer function ``r_E = Phi(mu_E, sigma_E)`` cannot be expressed in YAML and is therefore approximated as a sigmoid for backend code generation.
Cakan-Obermayer ALN
Backend emits standalone simulator source code from a model specification.
Symbolic code generation
Continuation solver
4D model describing the Ott-Antonsen reduction of infinite all-to-all coupled QIF neurons (Theta-neurons). Note: the original equations describe the dynamics of the Kuramoto parameter $Z$. Using the conformal transformation $Z=(1-W^\star)/(1+W^\star)$ and $W= \pi r + i V$, we express the system dynamics in terms of two state variables $r$ and $V$ representing the average firing rate and the average membrane potential of our QIF neurons. The conductance variable and its derivative are $g$ and $q$.
Coombes-Byrne
Coombes-Byrne 2D
Forward integration of a delay differential equation (e.g. long-range axonal delays).
DDE integration
Storage and interpolation of past states required for DDE/SDDE integration.
Delay history buffer
DifferentialEquations.jl integrators
Dumont-Gutkin
Dynamic equilibrium between excitatory and inhibitory drive.
E/I balance
Epileptor
Epileptor2D (E2D) is a phenomenological neural mass model consisting in the two-dimensional reduction ('x', 'z') of the original Epileptor model (see Epileptor5D; Proix et al., 2014, 2017). Note: ------ - Equations and default parameters are taken from (Proix et al.,2014), - The slow permittivity state-variable (z_E2D) can be modified to account for the time difference between the interictal (between seizures) and ictal (during seizure) states (see Proix et al., 2014).
Epileptor 2D
Stefanescu-McDonald reduced 3D Epileptor variant migrated as a placeholder scaffold from the legacy tvb-o.owl ontology. The original publication for this exact variant could not be located; the only OWL-side parameter is `N` (population size, grounded to "regulation of resting membrane potential"). Equations are minimal placeholders: a fast (x1) - slow (z) excitable system inspired by the canonical Epileptor (Jirsa et al. 2014), gated by N. This scaffold preserves the ontology lineage and parameter grounding; it should be replaced with the published equations once located, or removed if the variant is confirmed obsolete.
Epileptor 3D (Stefanescu-McDonald)
Epilepor5D (E5D) is a phenomenological, coupled, nonlinear five-dimensional (i.e., five state-variables ('x1', 'y1', 'z', 'x2', 'y2')) neural mass model able to realistically reproduce the temporal dynamics of epileptic seizures and the alternating sequence of seizures (ictal and interictal state; Jirsa et al.,2014; El Houssaini et al., 2015, 2020). Epileptor5D comprises three different time scales interacting together and accounting for various electrographic patterns: - the fastest and intermediate time scales are two coupled oscillators ((x1, y1) and (x2, y2)), accounting respectively for the low-voltage fast discharges (i.e., very fast oscillations) and spike-and-wave discharges. - the slowest time scale is responsible for leading the autonomous switch between interictal and ictal states and is driven by a slow-permittivity variable z. This switching is accompanied by a direct current (DC) shift that has been recorded in vitro and in vivo. The main output of the model: -x1+ x2, bears analogy with the field potential, while the precise biophysical equivalent of the z variable is unknown and will be likely complex. Note: ------ - Equations and default parameters are taken from (Jirsa et al.,2014 & El Houssaini et al., 2015), - The integral coupling function g(x1) can be rewritten as an ordinary differential equation, which is technically introduced, here, as a sixth state-variable (see Jirsa et al.,2014), - The slow permittivity state-variable (z_E5D) can be modified to account for the time difference between the interictal (between seizures) and ictal (during seizure) states (see Proix et al., 2014).
Epileptor 5D
Epileptor Resting-State (ERS) is an extension of the phenomenological neural mass model of Epileptor5D, tuned to express regionally specific physiological oscillations in addition to the epileptiform discharges (Courtiol et al., 2020). This extension was made using the Generic 2-dimensional Oscillator model (parametrized close to a supercritical Hopf Bifurcation) (Sanz-Leon et al., 2013, 2015) to reproduce the spontaneous local field potential-like signal. This model, its motivation and derivation can be found in the published article (Courtiol et al., 2020).
Epileptor (resting-state)
Spike-based or hybrid event-driven simulation of a neural network.
Event-driven integration
Generic depolarising synaptic effect; abstract parent of model-specific excitatory currents and conductances.
Excitation
Excitatory/inhibitory subpopulations
Fast-slow decomposition
Multiplicative gain applied to recurrent feedback. Control-theoretic concept with no faithful upstream GO mapping.
FeedbackScaling
GPU execution
4D model describing the Ott-Antonsen reduction of infinite all-to-all coupled QIF neurons (Theta-neurons) with Synaptic Depression adaptation mechanisms [Gastetal_2020]_. The two state variables :math:`r` and :math:`V` represent the average firing rate and the average membrane potential of our QIF neurons. :math:`A` and :math:`B` are respectively the adaptation variable and its derivative. The equations of the infinite QIF 2D population model read .. math:: \dot{r} &= 1/\tau (\Delta/(\pi \tau) + 2 V r)\\ \dot{V} &= 1/\tau (V^2 - \tau^2 \pi^2 r^2 + \eta + J \tau r (1 - A) + I)\\ \dot{A} &= 1/\tau_A (B)\\ \dot{B} &= 1/\tau_A (-2 B - A + \alpha r) \\ .. [Gastetal_2020] Gast, R., Schmidt, H., & Knösche, T. R. (2020). A mean-field description of bursting dynamics in spiking neural networks with short-term adaptation. *Neural Computation*, 32(9), 1615-1634.
Gast-Schmidt-Knosche (synaptic depression)
Gast-Schmidt-Knosche (synaptic facilitation)
The Generic 2-Dimensional Oscillator (G2D) is a phenomenological, coupled, nonlinear two-dimensional (i.e., two state-variables ('V', 'W')) oscillatory, neural mass model. The G2D is a generalization of the well-known FitzHugh-Nagumo model (FitzHugh, 1961; Nagumo et. al, 1962), adapted here for reproducing a wilder class of dynamical configurations of physiological phenomena as observed in neuronal population using phase-portrait method.
Generic 2-D oscillator
The Generic Linear (GL) is a phenomenological, linear one-dimensional (i.e., one state-variable ('x')) neural mass model (Galán, 2008).
Generic linear
Gradient-based optimisation
The Hopfield neural network is a discrete time dynamical system composed of multiple binary nodes, with a connectivity matrix built from a predetermined set of patterns. The update, inspired from the spin-glass model (used to describe magnetic properties of dilute alloys), is based on a random scanning of every node. The existence of a fixed point dynamics is guaranteed by a Lyapunov function. The Hopfield network is expected to have those multiple patterns as attractors (multistable dynamical system). When the initial conditions are close to one of the 'learned' patterns, the dynamical system is expected to relax on the corresponding attractor. A possible output of the system is the final attractive state (interpreted as an associative memory). Various extensions of the initial model have been proposed, among which a noiseless and continuous version [Hopfield 1984] having a slightly different Lyapunov function, but essentially the same dynamical properties, with more straightforward physiological Interpretation. A continuous Hopfield neural network (with a sigmoid transfer function) can indeed be interpreted as a network of neural masses with every node corresponding to the mean field activity of a local brain region, with many bridges with the Wilson Cowan model [WC_1972]. Note: - This model uses the modifications implemented by Golos et al. (2015).
Hopfield
Generic hyperpolarising synaptic effect; abstract parent of model-specific inhibitory currents and conductances.
Inhibition
Within-population synaptic coupling, distinct from long-range inter-node coupling.
InternalCoupling
JAX
Just-in-time compilation
Jansen 1995
The Jansen-Rit is a neurophysiologically-inspired neural mass model of a cortical column (or area), developed to simulate the electrical brain activity, i.e., the electroencephalogram (EEG), and evoked-potentials (EPs; Jansen et al., 1993; Jansen & Rit, 1995). It is a 6-dimensional, non-linear, model describing the local average states of three interconnected neural populations: pyramidal cells (PCs), excitatory and inhibitory interneurons (EINs and IINs), interacting through positive and negative feedback loops. The main output of the model is the average membrane potential of the pyramidal cell population, as the sum of the potential of these cells is thought to be the source of the potential recorded in the EEG.
Jansen-Rit
Lumped-parameter cortical column model with three neural populations: pyramidal cells, excitatory interneurons, and inhibitory interneurons. Each population is modeled by a PSP block (2nd-order linear filter) and a sigmoidal potential-to-firing-rate transformation. The model produces EEG-like output as the pyramidal PSP difference v_pyr = y1 - y2. Based on Lopes da Silva et al. (1974, 1976) and Jansen et al. (1993).
Jansen-Rit 1995
Julia JIT
It describes the mean-field activity of a population of Hodgkin-Huxley-type neurons (Depannemaker et al 2022) linking the slow fluctuations of intra- and extra-cellular potassium ion concentrations to the mean membrane potential, and the synaptic input to the population firing rate. The model is derived as the mathematical limit of an infinite number of all-to-all coupled neurons, resulting in 5 state variables: :math:`x` represents a phenomenological variable connected to the firing rate, :math:`V` represent the average membrane potential, :math:`n` represents the gating variable for potassium K, :math:`\Delta K_{int}` represent the intracellular potassium concentration, :math:`K_g` represents the extracellular potassium buffering by the external bath """
K-ion exchange
The Kuramoto model is a model of synchronization phenomena derived by Yoshiki Kuramoto in 1975 which has since been applied to diverse domains including the study of neuronal oscillations and synchronization.
Kuramoto
Kuramoto (extended)
The Larter-Breakspear is an extension (Breakspear et al., 2003a, 2003b) of the biophysical-inspired neural mass model of a cortical column (or area) from Larter et al. (1999), initially developed to simulate firing rate activity from focal region involved in partial seizure. It is determined by voltage- and ligand-gated ions channels and feedback between intensively interconnected excitatory and inhibitory neurons. The Larter-Breakspear is a 3D model describing the local average states of two interconnected neural populations: pyramidal cells (PCs) and inhibitory interneurons (IINs), with an additional variable representing the potassium channels in the population of PCs. The membrane potential of the pyramidal cells is the focus of the model and is governed by sodium, potassium, calcium and “leaky” ion channels, of which the voltage-gated potassium channels are modelled in more detail. The excitatory to excitatory connections are modelled in more detail as glutamatergic connections with AMPA and NMDA receptors. Note: - Equations and default parameters are taken from (Breakspear et al., 2003b), - All equations and parameters are non-dimensional and normalized to neural capacitance C = 1.
Larter-Breakspear
JR model with canonical parameters; long-range input is injected via a coupling term. Output is pyramidal potential difference.
Montbrio-Pazo-Roxin
NetworkDynamics.jl
NetworkX topology API
NumPy execution
Tracking of equilibria, periodic orbits, or invariant manifolds as a parameter is varied.
Numerical continuation
Forward integration of a deterministic ordinary differential equation.
ODE integration
Parameter exploration
PyRates
Forward integration of a random differential equation: parameters drawn from a distribution (Bayesian / ensemble simulation).
RDE integration
Reduced WongWang (RWW) is a biologically-inspired one-dimensional (i.e., only one state-variable 'S') neural mass model that approximates the realistic temporal dynamics of a detailed spiking and conductance-based synaptic large-scale network (Deco et al., 2013). RWW is the dynamical mean-field (DMF) reduction of the Reduced WongWang Exc-Inh model, that consists in disentangling the contribution of the two neuronal populations (excitatory and inhibitory) in order to study the time evolution of just one pool of neurons for each network node (Wong & Wang, 2006). It results that the dynamics of each network node described the temporal evolution of the opening probability of the NMDA channels.
Reduced Wong-Wang
Reduced WongWang Exc-Inh (RWW_EI) is a biologically-inspired two-dimensional (i.e., two state-variables ('S_E','S_I')) neural mass model describing the dynamics of a cortical area consisting of local networks of excitatory (AMPA) and inhibitory (GABA-A) populations of spiking neurons interconnected via NMDA synapses. These neurons are organized into an inhibitory population accounting for 20% of the neurons and an excitatory population accounting for 80% of the neurons.
Reduced Wong-Wang (E/I)
Reduced Wong-Wang (functional)
The Reduced Wong-Wang (RWW) model is a biophysically-based neural mass model that describes the dynamics of NMDA-mediated synaptic gating (Deco et al., 2013). It captures the slow dynamics relevant for resting-state fMRI and has been widely used for modeling whole-brain functional connectivity. RWW is the dynamical mean-field (DMF) reduction of the Wong & Wang (2006) spiking model. Each network node is described by a single state variable S representing the average NMDA synaptic gating (fraction of open channels). The total synaptic input x combines local excitatory recurrence (w), external input (I_o), and long-range network coupling (instant and delayed) scaled by the synaptic coupling strength J_N. The sigmoid transfer function H(x) maps the total input current to the average population firing rate. In a network setting, the global coupling strength G (a coupling parameter) scales the structural connectivity weights that modulate inter-regional communication.
Reduced Wong-Wang (tvboptim)
Forward integration of a stochastic delay differential equation.
SDDE integration
Forward integration of a stochastic differential equation.
SDE integration
Sigmoid activation
Reduced set of FitzHugh-Nagumo oscillators (Stefanescu & Jirsa 2008). A low-dimensional description of globally coupled heterogeneous excitatory and inhibitory FitzHugh-Nagumo populations. The reduction projects an infinite population onto `number_of_modes` modes and yields per-mode scalar state variables (xi, eta) for the excitatory and (alpha, beta) for the inhibitory subnetwork. The mode-coupling matrices Aik, Bik, Cik and the per-mode inputs IE_i, II_i, m_i, n_i, e_i, f_i are derived offline by Gaussian quadrature over the heterogeneity distribution N(mu, sigma^2); see TVB reference implementation `tvb.simulator.models.stefanescu_jirsa.ReducedSetFitzHughNagumo`.
Stefanescu-Jirsa 2D
Reduced set of Hindmarsh-Rose oscillators (Stefanescu & Jirsa 2008). A low-dimensional description of globally coupled heterogeneous excitatory and inhibitory Hindmarsh-Rose populations. The reduction projects an infinite population onto `number_of_modes` modes and yields per-mode scalar state variables (xi, eta, tau) for the excitatory and (alpha, beta, gamma) for the inhibitory subnetwork. The mode-coupling matrices A_ik, B_ik, C_ik and the per-mode coefficients a_i, b_i, c_i, d_i, e_i, f_i, h_i, p_i, IE_i, II_i, m_i, n_i are derived offline by Gaussian quadrature over the heterogeneity distribution N(mu, sigma^2); see TVB reference implementation `tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose`.
Stefanescu-Jirsa 3D
Stiff solver
Stochastic solver
Supercritical Hopf (SUPH) is an oscillatory/excitable neural mass model that describe the normal form of a supercritical Hopf bifurcation in Cartesian coordinates (Kuznetsov, 2013; Deco et al., 2017). This normal form has a supercritical bifurcation at 'a=0' with 'a' the bifurcation parameter in the model. So for 'a < 0', the local dynamics has a stable fixed point and the system corresponds to a damped oscillatory state, whereas for 'a > 0', the local dynamics enters in a stable limit cycle and the system switches to an oscillatory state.
Supercritical Hopf
TVB
Time-constant or rate parameter that sets the temporal scale of a dynamical variable. No faithful upstream GO mapping (GO_0050789 was rejected as overly generic).
TimeScaling
Tsodyks-Markram mean-field model of short-term synaptic plasticity. A three-variable neural mass model with firing rate E, synaptic depression variable x, and facilitation variable u. The transfer function uses a softplus nonlinearity α·log(1 + exp(s/α)). Reproduces the dynamics from BifurcationKit.jl/examples/TMModel.jl.
Tsodyks-Markram
Vectorised RNG
The Wilson and Cowan model consists of two populations or masses, one excitatory and one inhibitory, that are described by their mean firings rates E and I respectively (Wilson and Cowan, 1972, 1973). This model is the minimal representation of a NMM with a coarse-grained description of the overall activity of a large-scale neuronal network, as opposed to being a detailed biophysical model. While employing just two differential equations, it has been used to build various biophysically realistic models (Liley et al., 1999; Daffertshofer and van Wijk, 2011). Key parameters in the model are the strength of connectivity between each subtype of population (excitatory and inhibitory) and the strength of input to each subpopulation. The Input parameters P and Q also provide the entry point for local and long-range connectivity, that is, the activity coming from neighboring and distant populations respectively. Varying Input and connectivity generates a diversity of dynamical behaviors that are representative of observed activity in the brain, like multistability, oscillations, traveling waves and spatial patterns. We consider the transmission parameters of the excitatory population to be glutamatergic and therefore to be modified by glutamatergic receptors. The inhibitory population is considered as GABAergic. Note: - Equations and parameter names are taken from (Wilson and Cowan, 1972 and Sanz-Leon et al., 2015) - Default parameters are taken from Fig. 4 p.10 (Wilson and Cowan, 1972) - The model in Sanz-Leon et., 2015 includes more parameters than the original model, which can be traced in the description of the parameters.
Wilson-Cowan
Zerlaut (first-order adaptation)
Zetterberg-Jansen
tvboptim
Excitatory population firing rate. In full aLN this is ``Phi(mu_E_eff, s_EE)`` with ``Phi`` precomputed; here a sigmoid stand-in is used.
Inhibitory population firing rate (sigmoid stand-in for aLN Phi).
Membrane capacitance of a single AdEx neuron.
Reversal potential of the adaptation current.
Leak reversal potential of a single AdEx neuron.
Subthreshold adaptation conductance amplitude (a in AdEx).
Maximum excitatory-to-excitatory PSC amplitude (Jee_max in neurolib).
Maximum inhibitory-to-excitatory PSC amplitude (Jei_max in neurolib, signed negative).
Maximum excitatory-to-inhibitory PSC amplitude (Jie_max in neurolib).
Maximum inhibitory-to-inhibitory PSC amplitude (Jii_max in neurolib, signed negative).
Steepness of the sigmoid transfer-function surrogate.
Mean external input current to the excitatory population (mue_ext_mean).
Mean external input current to the inhibitory population (mui_ext_mean).
Half-maximum input of the sigmoid transfer-function surrogate.
Asymptotic firing rate used in the sigmoid surrogate for the aLN transfer function (sigmoid stand-in for the precomputed lookup table).
Standard deviation of the random-coupling-driven membrane-potential fluctuations entering the E->E channel (sigmae_ext component).
Standard deviation of the random-coupling-driven membrane-potential fluctuations entering the I->E channel.
Standard deviation of the random-coupling-driven membrane-potential fluctuations entering the E->I channel.
Standard deviation of the random-coupling-driven membrane-potential fluctuations entering the I->I channel.
Time constant of the adaptation current.
Synaptic time constant of excitatory PSCs (E->E and E->I).
Synaptic time constant of inhibitory PSCs (I->E and I->I).
Filtered mean synaptic input current to the excitatory population.
(J_EE*r_E + J_EI*r_I + mu_E + c_pop0 - I_A_var/C - mu_se)/tau_se
Filtered mean synaptic input current to the inhibitory population.
(J_IE*r_E + J_II*r_I + mu_I - mu_si)/tau_si
Half-width of heterogeneous noise distribution
Constant parameter to scale the rate of feedback from the firing rate variable to itself
Half-width of heterogeneous noise distribution over excitatory population
Half-width of heterogeneous noise distribution over inhibitory population
Ratio of excitatory VS inhibitory global couplings G_ie/G_ee
External homogeneous current on excitatory population
External current on inhibitory population
Mean heterogeneous current on excitatory population
Mean heterogeneous current on inhibitory population
Characteristic time of excitatory population
Characteristic time of inhibitory population
(I_e + V_e**2 + eta_e - pi**2*r_e**2*tau_e**2 + s_ee*tau_e - s_ei*tau_e)/tau_e
(I_i + V_i**2 + eta_i - pi**2*r_i**2*tau_i**2 + s_ie*tau_i - s_ii*tau_i)/tau_i
External input current to the first state-variable x_E2D, in Epileptor2D (Proix et al
External input current to the first state-variable x_E2D, in Epileptor2D (Proix et al.,2014).
Permittivity coupling on the slow permittivity state-variable z_E2D in Epileptor2D (Proix et al
Permittivity coupling on the slow permittivity state-variable z_E2D in Epileptor2D (Proix et al., 2014). The coupling quantifies the influence of neuronal fast discharges x_E2D on the local slow permittivity variable z_E2D.
Coupling scaling on a very fast time scale
Coefficient of the cubic term in the first state-variable x_E2D via the function f(x)_E2D, in Epileptor2D Proix et al
Coefficient of the cubic term in the first state-variable x_E2D via the function f(x)_E2D, in Epileptor2D Proix et al.,2014).
Coefficient of the squared term in the first state-variable x_E2D via the function f_E2D, in Epileptor2D (Proix et al
Coefficient of the squared term in the first state-variable x_E2D via the function f_E2D, in Epileptor2D (Proix et al.,2014).
Additive coefficient for the second state-variable x_{2}, called :math:`y_{0}` in Jirsa paper
Coefficient of the squared term in the first state-variable x_E2D via the function f in Epileptor2D (Proix et al
Coefficient of the squared term in the first state-variable x_E2D via the function f in Epileptor2D (Proix et al.,2014).
When modification is True, the function h_E2D uses a nonlinear influence on z_E2D
When modification is True, the function h_E2D uses a nonlinear influence on z_E2D. The default value is False, i.e., linear influence on z_E2D.
Temporal scaling in the slow state-variable, \ called :math:`1\tau_{0}` in Jirsa paper (see class Epileptor)
Linear coefficient in the first state-variable x_E2D via the function f_E2D, in Epileptor2D (Proix et al
Linear coefficient in the first state-variable x_E2D via the function f_E2D, in Epileptor2D (Proix et al.,2014).
Characteristic time scale of the whole-system Epileptor2D
Degree of excitability or epileptogenicity in Epileptor2D (Proix et al
Degree of excitability or epileptogenicity in Epileptor2D (Proix et al.,2014). If x0 is greater than a critical value, x0,critic = −2.05 (i.e., in the supercritical regime), the system can trigger seizures autonomously; otherwise it is in its equilibrium state (i.e., in the subcritical regime). Note: - If modification_E2D: if x0 is smaller than a critical value, x0,critic = 2.91, can trigger seizures autonomously; otherwise it is in its equilibrium state (Proix et al., 2014).
First state-variable in the Epileptor2D (Proix et al
tt*(Iext + Kvf*c_pop0 + c + local_coupling*x1 - x1*x1cond - z)
Slow permittivity state-variable of the Epileptor2D (Proix et al
N (Epileptor3DStefanescuMcDonald parameter)
Effective population size / scaling of the resting-state regulation term.
tau_z (Epileptor3DStefanescuMcDonald parameter)
Slow timescale for the z permittivity variable.
x_0 (Epileptor3DStefanescuMcDonald parameter)
Excitability parameter (Epileptor convention).
Fast excitable variable (placeholder Epileptor-like dynamics).
x1 (Epileptor3DStefanescuMcDonald state variable)
Slow permittivity / excitability variable.
z (Epileptor3DStefanescuMcDonald state variable)
External input current to the first sub-population (x1_E5D, y1_E5D) via the state-variable x1_E5D, in Epileptor5D (Jirsa et al
External input current to the first sub-population (x1_E5D, y1_E5D) via the state-variable x1_E5D, in Epileptor5D (Jirsa et al.,2014).
External input current to the second population
Correspond to the coupling scaling on a fast time scale
Permittivity coupling on the slow permittivity state-variable z_E5D in Epileptor5D (Proix et al
Permittivity coupling on the slow permittivity state-variable z_E5D in Epileptor5D (Proix et al., 2014). The coupling quantifies the influence of neuronal fast discharges x1_E5D on the local slow permittivity variable z_E5D.
Coefficient of the cubic term in the first state variable
Linear coefficient in fifth state variable
Coefficient of the squared term in the first state variabel
Linear coefficient of lowpass excitatory coupling in fourth state variable
Additive coefficient for the second state variable, called :math:`y_{0}` in Jirsa paper
Coefficient of the squared term in the derivative of the second state-variable y1_E5D in Epileptor5D (Jirsa et al
Coefficient of the squared term in the derivative of the second state-variable y1_E5D in Epileptor5D (Jirsa et al.,2014).
When modification is True, the function h_E5D uses a nonlinear influence on z_E5D
When modification is True, the function h_E5D uses a nonlinear influence on z_E5D. The default value is False, i.e., linear influence on z_E5D.
Temporal scaling in the third state variable, called :math:`1/\tau_{0}` in Jirsa paper
Linear coefficient in the slow permittivity state-variable z_E5D in Epileptor5D (Jirsa et al
Linear coefficient in the slow permittivity state-variable z_E5D in Epileptor5D (Jirsa et al.,2014).
Linear coefficient in the first state variable
Temporal scaling coefficient in fifth state variable
Characteristic time scale of the whole-system Epileptor5D
Epileptogenicity Parameter. Degree of excitability or epileptogenicity in Epileptor5D (Jirsa et al.,2014). If x0 is greater than a critical value, x0,critic = −2.05, the system can trigger seizures autonomously; otherwise it is in its equilibrium state. Note: - If modification_E5D: if x0 is smaller than a critical value, x0,critic = 2.91, can trigger seizures autonomously; otherwise it is in its equilibrium state (Proix et al., 2014).
g_E5D is a low-pass filtered excitatory coupling function from sub-population (x1_E5D, y1_E5D) to (x2_E5D, y2_E5D) in Epileptor5D (Jirsa et al
First state-variable of the first Epileptor5D population (Jirsa et al
tt*(Iext + Kvf*c_pop0 + local_coupling*x1 + x1*x1cond + y1 - z)
First state-variable of the second Epileptor5D sub-population (Jirsa et al
Second state-variable of the first Epileptor5D sub-population (Jirsa et al
Second state-variable of the second Epileptor5D population (Jirsa et al
Slow permittivity state-variable of the Epileptor5D (Jirsa et al
lc_1 (EpileptorRestingState derived variable)
output (EpileptorRestingState derived variable)
x1cond (EpileptorRestingState derived variable)
y2cond (EpileptorRestingState derived variable)
zcond (EpileptorRestingState derived variable)
External input current to the third population (x_rs, y_rs)
External input current to the first population (x1, y1)
External input current to the second population (x2, y2)
Permittivity coupling, that is from the very fast time scale toward the slow time scale
Coefficient of the cubic term in the first state-variable x1
Vertical shift of the configurable nullcline in the state-variable y_rs
Linear coefficient in the fifth state-variable y2
Constant parameter to scale the rate of feedback from the slow variable y_rs to the fast variable x_rs
alpha_rs (EpileptorRestingState parameter)
Coefficient of the squared term in the first state-variable x1
Linear coefficient of the state-variable y_rs
Linear coefficient of lowpass excitatory coupling in the fourth state-variable x2
Constant parameter to scale the rate of feedback from the slow variable y_rs to itself
beta_rs (EpileptorRestingState parameter)
Additive coefficient for the second state-variable y1, called :math:'y_{0}' in Jirsa et al
Additive coefficient for the second state-variable y1, called :math:'y_{0}' in Jirsa et al. (2014).
Coefficient of the squared term in the second state-variable y1
Temporal scaling of the whole third system (x_rs, y_rs)
Coefficient of the squared term in the sixth state-variable x_rs
Coefficient of the cubic term in the sixth state-variable x_rs
Constant parameter to reproduce FHN dynamics where excitatory input currents are negative
gamma_rs (EpileptorRestingState parameter)
Constant parameter to reproduce FHN dynamics where excitatory input currents are negative. Note: It scales both I_rs and the long-range coupling term.
Temporal scaling in the third state-variable z, called :math:'1/ au_{0}' in Jirsa et al
Temporal scaling in the third state-variable z, called :math:'1/ au_{0}' in Jirsa et al. (2014).
Linear coefficient in the first state-variable x1
Temporal scaling coefficient in the fifth state-variable y2
Temporal scaling coefficient in the third population (x_rs, y_rs)
tau_rs (EpileptorRestingState parameter)
g (EpileptorRestingState state variable)
x1 (EpileptorRestingState state variable)
x2 (EpileptorRestingState state variable)
x_rs (EpileptorRestingState state variable)
d_rs*tau_rs*(I_rs*gamma_rs + K_rs*c_pop2*gamma_rs + alpha_rs*y_rs + e_rs*x_rs**2 - f_rs*x_rs**3 + lc_1)
y1 (EpileptorRestingState state variable)
y2 (EpileptorRestingState state variable)
y_rs (EpileptorRestingState state variable)
d_rs*(a_rs + b_rs*x_rs - beta_rs*y_rs)/tau_rs
z (EpileptorRestingState state variable)
r*tt*(Ks*c_pop0 - 4*x0 + 4*x1 - z + zcond)
It is the weight on Coupling through variable r
It is the weight on Coupling through variable V
Mean of heterogeneous noise distribution
A (GastSchmidtKnosche_SD state variable)
B (GastSchmidtKnosche_SD state variable)
V (GastSchmidtKnosche_SD state variable)
r (GastSchmidtKnosche_SD state variable)
A (GastSchmidtKnosche_SF state variable)
B (GastSchmidtKnosche_SF state variable)
V (GastSchmidtKnosche_SF state variable)
r (GastSchmidtKnosche_SF state variable)
Vertical shift of the configurable nullcline
Constant parameter to scale the rate of feedback from the slow variable to the fast variable.
alpha_G2D is the coefficient of the linear feedback coupling term of the state-variable W_G2D to the first state-equation Vdot_G2D. Constant parameter to scale the rate of feedback from the slow variable to the fast variable.
Linear slope of the configurable nullcline
Constant parameter to scale the rate of feedback from the slow variable to itself
Parabolic term of the configurable nullcline
Temporal scale factor. Warning: do not use it unless you know what you are doing and know about time tides.
Coefficient of the quadratic term of the cubic nullcline
Coefficient of the cubic term of the cubic nullcline
Coefficient of the linear term of the cubic nullcline
Constant parameter to reproduce FHN dynamics where excitatory input currents are negative. It scales both I and the long range coupling term.
A time-scale hierarchy can be introduced for the state variables :math:`V` and :math:`W`
A time-scale hierarchy can be introduced for the state variables :math:`V` and :math:`W`. Default parameter is 1, which means no time-scale hierarchy.
V_G2D is the first state-variable of the Generic 2-Dimensional Oscillator model, that can be associated with membrane potentials and sodium activation (FitzHugh, 1961)
d*tau*(I*gamma - V**3*f + V**2*e + V*g + V*local_coupling + W*alpha + c_glob*gamma)
W_G2D is the second state-variable of the Generic 2-Dimensional Oscillator model, that can be associated with accommodation and refractoriness and considered to represent potassium activation, sodium inactivation, or both (FitzHugh, 1961)
Slope parameter of the state-variable 'x_GL', representing the inverse of the relaxation time
Slope parameter of the state-variable 'x_GL', representing the inverse of the relaxation time. Note: The ‘minus’ sign for the self-connections, scaled by 'gamma_GL', guarantees that the nodes have a stable equilibrium point (ref).
State-variable of the Generic Linear model
The slow time-scale for threshold calculus :math:`\\theta`, state-variable of the model
The fast time-scale for potential calculus :math:`x`, state-variable of the model
Sigmoid function that transforms the average membrane potential of the PCs populations (y0) into an average firing rate to the excitatory interneurons EINs.
Sigmoid function that transforms the average membrane potential of the PCs populations (y0) into an average firing rate to the inhibitory interneurons IINs.
Sigmoid function that transforms the average membrane potential of the interneurons populations (y1-y2) into an average firing rate to the PCs.
sigma_y1_y2 (JansenRit derived variable)
Maximum amplitude of EPSP [mV]. Also called average synaptic gain.
Maximum amplitude of IPSP [mV]. Also called average synaptic gain.
Average number of synapses between three neuronal populations of the model
Average number of synapses between three neuronal populations of the model. It accounts for synaptic phenomena such as neurotransmitter depletion.
Reciprocal of the time constant of passive membrane and all other spatially distributed delays in the dendritic network. Also called average synaptic time constant.
Rate constant of the excitatory post-synaptic potential (EPSP). It is interpreted as being the lumped representation of the sum of the reciprocal of the time constant of passive membrane and all other spatially distributed delays, including temporal dispersion in the afferent tract, synaptic diffusion, and resistive-capacitive delay in the dendritic network.
Average probability constant of the number of synapses made by the pyramidal cells to the dendrites of the excitatory interneurons (feedback excitatory loop)
Average probability constant of the number of synapses made by the pyramidal cells to the dendrites of the excitatory interneurons (feedback excitatory loop). It characterizes the connectivity between the PCs and EINs.
Average probability constant of the number of synapses made by the EINs to the dendrites of the PCs
Average probability constant of the number of synapses made by the EINs to the dendrites of the PCs. It characterizes the excitatory connectivity between the EINs and PCs.
Average probability constant of the number of synapses made by the PCs to the dendrites of the IINs
Average probability constant of the number of synapses made by the PCs to the dendrites of the IINs. It characterizes the connectivity between the PCs and inhibitory IINs.
Average probability constant of the number of synapses made by the IINs to the dendrites of the PCs
Average probability constant of the number of synapses made by the IINs to the dendrites of the PCs. It characterizes the connectivity between the IINs and PCs.
Rate constant of the inhibitory post-synaptic potential (IPSP)
Rate constant of the inhibitory post-synaptic potential (IPSP). It is interpreted as being the lumped representation of the sum of the reciprocal of the time constant of passive membrane and all other spatially distributed delays, including temporal dispersion in the afferent tract, synaptic diffusion, and resistive-capacitive delay in the dendritic network (Freeman 1975).
Mean excitatory external input to the derivative of the state-variable y4_JR (PCs) represented by a pulse density, that consists of activity originating from adjacent and more distant cortical columns, as well as from subcortical structures (e
Mean excitatory external input to the derivative of the state-variable y4_JR (PCs) represented by a pulse density, that consists of activity originating from adjacent and more distant cortical columns, as well as from subcortical structures (e.g., thalamus).
Asymptotic of the sigmoid function Sigm_JR corresponds to the maximum firing rate of the neural populations
Steepness (or gain) parameter of the sigmoid function Sigm_JR
Average firing threshold (PSP) for which half of the firing rate is achieved
Average firing threshold (PSP) for which half of the firing rate is achieved. Note: - The usual value for this parameter is 6.0 (Jansen et al., 1993; Jansen & Rit, 1995).
First state-variable of the first Jansen-Rit population
First state-variable of the second Jansen-Rit population (EINs)
First state-variable of the third Jansen-Rit population (IINs)
Second state-variable of the first Jansen-Rit population (excitatory PCs)
Second state-variable of the second excitatory Jansen-Rit population (excitatory EINs)
Second state-variable of the third (inhibitory) Jansen-Rit population (IINs)
Aggregate pyramidal PSP (EEG-like output)
Maximum amplitude of excitatory PSP (EPSP)
Maximum amplitude of inhibitory PSP (IPSP)
Half of maximum firing rate (2*e0 = max rate)
Mean external input (afferent pulse density)
PSP for 50% firing rate (sigmoid midpoint)
A*a*(p + C2*Sigm(C1*y0) + c_intercolumn) - 2*a*y4 - a**2*y1
(V - 26.64*log(Na_o/Na_i))*(g_Na*h*minf + g_Nal)
Piecewise((R_minus*c_pop0*(-V + E)/pi - R_minus*x**2 + V_temp + eta, V <= Vstar), (R_minus*c_pop0*(-V + E)/pi - R_plus*x**2 + V_temp + eta, True))
Piecewise((Delta - J*r*x + 2*R_minus*x*(V - c_minus), V <= Vstar), (Delta - J*r*x + 2*R_plus*x*(V - c_plus), True))
initial concentration of intracellular Cl
initial concentration of extracellular Cl
initial concentration of intracellular K
initial concentration of extracellular K
initial concentration of intracellular Na
initial concentration of extracellular Na
Input via local and long range connectivity, passing first through the Kuramoto coupling function
Sets the base line frequency for the Kuramoto oscillator
The Kuramoto model has only one state variable representing oscillations in general
`I` is the input via local and long range connectivity, passing first through the Kuramoto coupling function
signal (KuramotoModel2 derived variable)
Sets the base line frequency for the Kuramoto oscillator in [rad/ms].
Average firing rate of the population of PCs, V.
0.5*Q_Vmax*(tanh((V - V_T)/delta_V) + 1)
Average firing rate of the population of IINs, Z.
0.5*Q_Zmax*(tanh((Z - Z_T)/delta_Z) + 1)
lc_0 (LarterBreakspear derived variable)
Fraction of open calcium channels (Breakspear et al., 2003b). Describes relationship between membrane voltage and channel conductance.
m_Ca (LarterBreakspear derived variable)
Fraction of open potassium channels (Breakspear et al., 2003b). Describes relationship between membrane voltage and channel conductance.
Fraction of open sodium channels (Breakspear et al., 2003b). Describes relationship between membrane voltage and channel conductance.
m_Na (LarterBreakspear derived variable)
Coupling scaling factor. Strength of excitatory coupling and balance between internal and local/global coupling strength.
Subcortical input current. Represents a non-specific excitation or thalamic inputs.
Maximal firing rate for excitatory populations
Maximal firing rate for inhibitory population
Threshold potential for excitatory neurons
Threshold potential for excitatory neurons. Describes the mean potential of the excitatory population.
Threshold potential (mean) for inihibtory neurons
Excitatory-to-excitatory synaptic strength
Excitatory-to-inhibitory synaptic strength
Inhibitory-to-excitatory synaptic strength
Non-specific-to-excitatory synaptic strength
Non-specific-to-inhibitory synaptic strength
Variance of Calcium channel threshold. Defines the steepness of the sigmoid $m_{Ca}$.
Variance of Potassium channel threshold. Defines the steepness of the sigmoid $m_K$.
Variance of sodium channel threshold. Defines the steepness of the sigmoid $m_{Na}$.
Variance of excitatory threshold. Defines the steepness of sigmoid $Q_V$.
Variance of inhibitory threshold. Defines the steepness of sigmoid $Q_Z$.
Conductance of population of calcium (Ca++) channels
Conductance of population of calcium (Ca++) channels. Describes the maximal conductance if all channels are open.
Conductance of population of potassium (K) channels
Conductance of population of potassium (K) channels. Describes the maximal conductance if all channels are open.
Conductance of population of leak channels
Conductance of population of Na channels
Time constant for K relaxation time (ms)
First state-variable of the Larter-Breakspear (Breakspear et al
Second state-variable of the Larter-Breakspear (Breakspear et al
Third state-variable of the Larter-Breakspear (Breakspear et al
EEG-like output: pyramidal potential difference
v_pyr (ModelJansen1995 derived variable)
(I + J*r*tau + V**2 + c_pop0*cr*tau + c_pop1*cv + eta - pi**2*r**2*tau**2)/tau
Sigmoid input-output function of the synaptic input current, x_RWW, that computes the averaged firing rate of the neurons population (Deco et al., 2013).
(NMDAR)-mediated synaptic input current to the neurons population (Deco et al., 2013).
I_o + J_N*S*local_coupling + J_N*S*w + J_N*c_pop0
External input current to the neurons population (Deco et al
External input current to the neurons population (Deco et al., 2013). Note: ------ In TVB, the default value is set to 0.33 (instead of 0.30 as in Deco et al. (2013)) for setting the system is bistability (see Hansen et al., 2015).
Slope (or gain) parameter of the sigmoid input-output function H_RWW (Deco et al
Slope (or gain) parameter of the sigmoid input-output function H_RWW (Deco et al., 2013). Note: ----- In the original publication, the parameter 'a' is set in (nC)^-1 (i.e., equivalent to (nA.s)^-1). In TVB, the parameter has been converted in (pC)^-1 for setting the system in millisecond.
Shift parameter of the sigmoid input-output function H_RWW (Deco et al
Shift parameter of the sigmoid input-output function H_RWW (Deco et al., 2013). Note: ------ In the original publication, the parameter 'b' is set in Hz. In TVB, the parameter has been converted in kHz for setting the system in millisecond.
Scaling parameter of the sigmoid input-output function H_RWW (Deco et al
Scaling parameter of the sigmoid input-output function H_RWW (Deco et al., 2013). Note: ------ In the original publication, the parameter 'd' is set in second. In TVB, the parameter has been converted in millisecond for setting the system in millisecond.
Kinetic parameter. Decay time constant for the NMDA synapses of the neurons population (Deco et al., 2013).
State-variable of the Reduced WongWang model, denoting the average, (NMDAR)-mediated synaptic gating, i
Sigmoid function that computes the averaged firing rate of the excitatory neuron population IE_RWW_EI (Deco et al., 2014; Abbott & Chance, 2005).
H_e (ReducedWongWangExcInh derived variable)
Sigmoid function that computes the averaged firing rate of the inhibitory neuron population II_RWW (Deco et al., 2014; Abbott & Chance, 2005).
H_i (ReducedWongWangExcInh derived variable)
J_N_S_e (ReducedWongWangExcInh derived variable)
coupling (ReducedWongWangExcInh derived variable)
Input current to the excitatory population (Deco et al., 2014).
x_e (ReducedWongWangExcInh derived variable)
a_e*(I_ext + I_o*W_e + J_N_S_e*w_p - J_i*S_i + coupling) - b_e
Input current to the inhibitory population (Deco et al., 2014).
x_i (ReducedWongWangExcInh derived variable)
a_i*(I_o*W_i + J_N_S_e - S_i + coupling*lamda) - b_i
External stimulation parameter for evoked potential simulated activity applied to the excitatory population firing rate IE (Deco et al
External stimulation parameter for evoked potential simulated activity applied to the excitatory population firing rate IE (Deco et al., 2014).
Effective external input parameter to the excitatory E and inhibitory I population (Deco et al
Effective external input parameter to the excitatory E and inhibitory I population (Deco et al., 2014).
Excitatory synaptic coupling parameter (Deco et al
Excitatory synaptic coupling parameter (Deco et al., 2014).
Local feedback inhibitory synaptic coupling to the excitatory population firing rate IE (Deco et al
Local feedback inhibitory synaptic coupling to the excitatory population firing rate IE (Deco et al., 2014).
Excitatory population external input scaling weight
Inhibitory population external input scaling weight
Slope (or gain) parameter of the sigmoid function HE_RWW_EI (Deco et al
Slope (or gain) parameter of the sigmoid function HE_RWW_EI (Deco et al., 2014).
Slope (or gain) parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al
Slope (or gain) parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al., 2014).
Shift parameter of the sigmoid function HE_RWW_EI of the excitatory population (Deco et al
Shift parameter of the sigmoid function HE_RWW_EI of the excitatory population (Deco et al., 2014).
Shift parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al
Shift parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al., 2014).
Scaling parameter of the sigmoid function HE_RWW_EI of the excitatory population (Deco et al
Scaling parameter of the sigmoid function HE_RWW_EI of the excitatory population (Deco et al., 2014).
Scaling parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al
Scaling parameter of the sigmoid function HI_RWW_EI of the inhibitory population (Deco et al., 2014).
gamma_e (ReducedWongWangExcInh parameter)
gamma_i (ReducedWongWangExcInh parameter)
Kinetic parameter that represents the decay times for NMDA synapses (Deco et al
Kinetic parameter that represents the decay times for NMDA synapses (Deco et al., 2014). Excitatory population NMDA decay time constant.
Kinetic parameter that represents the decay times for inhibitory GABA synapses (Deco et al
Kinetic parameter that represents the decay times for inhibitory GABA synapses (Deco et al., 2014).
State-variable of the Reduced WongWang_EI model (Deco et al
S_e (ReducedWongWangExcInh state variable)
S_i (ReducedWongWangExcInh state variable)
Average population firing rate H evaluated at the total synaptic input x.
H_x (ReducedWongWangTvboptim derived variable)
Total synaptic input current combining local recurrence, external input, and network coupling (instant and delayed) scaled by J_N.
x (ReducedWongWangTvboptim derived variable)
w*J_N*S + I_o + J_N*instant + J_N*delayed
Effective external input current to each cortical population. Default 0.34 nA sets the system in bistability (Hansen et al., 2015). Together with w, this is a key parameter for fitting functional connectivity.
NMDA synaptic coupling strength. Scales both local recurrence and long-range network coupling inputs.
Input gain parameter of the sigmoid transfer function H(x).
Input shift parameter of the sigmoid transfer function H(x).
Scaling parameter of the sigmoid transfer function H(x).
gamma (ReducedWongWangTvboptim parameter)
Kinetic parameter governing the rate of NMDA channel dynamics.
tau_s (ReducedWongWangTvboptim parameter)
NMDA decay time constant for the synaptic gating variable S.
Excitatory recurrence strength (local feedback)
Local excitatory recurrence strength. Controls the positive feedback within each cortical population. Higher values increase local excitation and push the system toward higher firing rates.
Average NMDA synaptic gating variable, representing the fraction of open NMDA channels at each network node.
S (ReducedWongWangTvboptim state variable)
Excitatory-to-excitatory mode-coupling matrix derived from Gaussian quadrature over N(mu, sigma); computed offline at `update_derived_parameters`.
Aik (StefanescuJirsa2D derived variable)
Inhibitory-to-excitatory mode-coupling matrix.
Bik (StefanescuJirsa2D derived variable)
Excitatory-to-inhibitory mode-coupling matrix.
Cik (StefanescuJirsa2D derived variable)
Per-mode constant excitatory input from the heterogeneity distribution.
IE_i (StefanescuJirsa2D derived variable)
Per-mode constant inhibitory input from the heterogeneity distribution.
II_i (StefanescuJirsa2D derived variable)
Per-mode cubic-coefficient projection for the excitatory subnetwork.
e_i (StefanescuJirsa2D derived variable)
Per-mode cubic-coefficient projection for the inhibitory subnetwork.
f_i (StefanescuJirsa2D derived variable)
Per-mode offset entering the eta dynamics.
m_i (StefanescuJirsa2D derived variable)
Per-mode offset entering the beta dynamics.
n_i (StefanescuJirsa2D derived variable)
Internal coupling, excitatory to excitatory.
Internal coupling, inhibitory to excitatory.
Internal coupling, excitatory to inhibitory.
Heterogeneity scale parameter entering the m_i, n_i offsets.
Mean of the Gaussian heterogeneity distribution.
Standard deviation of the Gaussian heterogeneity distribution.
Timescale separation between fast (xi, alpha) and slow (eta, beta) variables.
alpha (StefanescuJirsa2D state variable)
tau*(alpha - f_i*alpha**3/3 - beta) + K21*(Cik*xi - alpha) + tau*(II_i + c_pop0 + local_coupling*xi)
Inhibitory slow recovery variable (per mode).
Excitatory slow recovery variable (per mode).
tau*(xi - e_i*xi**3/3 - eta) + K11*(Aik*xi - xi) - K12*(Bik*alpha - xi) + tau*(IE_i + c_pop0 + local_coupling*xi)
Excitatory-to-excitatory mode-coupling matrix from Gaussian quadrature.
A_ik (StefanescuJirsa3D derived variable)
B_ik (StefanescuJirsa3D derived variable)
C_ik (StefanescuJirsa3D derived variable)
IE_i (StefanescuJirsa3D derived variable)
II_i (StefanescuJirsa3D derived variable)
Per-mode cubic coefficient for the excitatory xi dynamics (a * <V|V^3>).
a_i (StefanescuJirsa3D derived variable)
Per-mode quadratic coefficient for the excitatory xi dynamics.
b_i (StefanescuJirsa3D derived variable)
Per-mode constant for the excitatory eta dynamics.
c_i (StefanescuJirsa3D derived variable)
Per-mode quadratic coefficient for the excitatory eta dynamics.
d_i (StefanescuJirsa3D derived variable)
Per-mode cubic coefficient for the inhibitory alpha dynamics.
e_i (StefanescuJirsa3D derived variable)
Per-mode quadratic coefficient for the inhibitory alpha dynamics.
f_i (StefanescuJirsa3D derived variable)
Per-mode constant for the inhibitory beta dynamics.
h_i (StefanescuJirsa3D derived variable)
Per-mode adaptation offset entering the tau dynamics.
m_i (StefanescuJirsa3D derived variable)
Per-mode adaptation offset entering the gamma dynamics.
n_i (StefanescuJirsa3D derived variable)
Per-mode quadratic coefficient for the inhibitory beta dynamics.
p_i (StefanescuJirsa3D derived variable)
Hindmarsh-Rose dimensionless coefficient a.
Hindmarsh-Rose dimensionless coefficient b.
Hindmarsh-Rose dimensionless coefficient c.
Hindmarsh-Rose dimensionless coefficient d.
Adaptation parameter (Hindmarsh-Rose r).
Leftmost equilibrium point of x in the Hindmarsh-Rose oscillator.
alpha (StefanescuJirsa3D state variable)
beta - e_i*alpha**3 + f_i*alpha**2 - gamma + K_21*(C_ik*xi - alpha) + II_i + c_pop0 + local_coupling*xi
Inhibitory recovery variable (per mode).
Excitatory recovery variable (per mode).
Inhibitory slow adaptation variable (per mode).
gamma (StefanescuJirsa3D state variable)
Excitatory slow adaptation variable (per mode).
eta - a_i*xi**3 + b_i*xi**2 - tau + K_11*(A_ik*xi - xi) - K_12*(B_ik*alpha - xi) + IE_i + c_pop0 + local_coupling*xi
First state-variable of the Supercritical Hopf model
Second state-variable of the Supercritical Hopf model
Total synaptic input (recurrent + external)
Softplus nonlinearity steepness (smoothed threshold gain)
Membrane time constant (firing rate relaxation)
Synaptic depression recovery time constant
Synaptic facilitation decay time constant
Mean firing rate of the excitatory population
Synaptic facilitation variable (release probability). Decays to U0 with time constant tauF and is enhanced proportionally to firing.
Synaptic depression variable (fraction of available neurotransmitter). Recovers to 1 with time constant tauD and is depleted proportionally to firing.
Average level of excitation in excitatory neuron at time t. Can be interpreted as membrane potential (Sanz-Leon et al., 2015).
alpha_e*(-I*c_ei + P + c_ee*E + c_pop0 + lc_0 + lc_1 - theta_e)
Average level of excitation in inhibitory neuron at time t. Can be interpreted as membrane potential. (Sanz-Leon et al., 2015).
alpha_i*(-I*c_ii + Q + c_ie*E + lc_0 + lc_1 - theta_i)
External stimulus to the excitatory population (Wilson and Cowan, 1972)
External stimulus to the inhibitory population (Wilson and Cowan, 1972)
Steepness of the excitatory response function S_e_WC (Wilson and Cowan, 1972)
Steepness of the excitatory response function S_i_WC (Wilson and Cowan, 1972)
Balance parameter between excitatory and inhibitory masses (Sanz-Leon et al
Balance parameter between excitatory and inhibitory masses (Sanz-Leon et al., 2015).
Position of the maximum slope of the excitatory response function S_e_WC (Sanz-Leon et al
Position of the maximum slope of the excitatory response function S_e_WC (Sanz-Leon et al., 2015).
Position of the maximum slope of a sigmoid function [in threshold units]
The amplitude parameter for the excitatory response function
Excitatory to excitatory coupling coefficient
Inhibitory to excitatory coupling coefficient
The amplitude parameter for the excitatory response function S_i_WC (Sanz-Leon et al
The amplitude parameter for the excitatory response function S_i_WC (Sanz-Leon et al., 2015).
Excitatory to inhibitory coupling coefficient (Sanz-Leon et al
Excitatory to inhibitory coupling coefficient (Sanz-Leon et al., 2015)
Inhibitory to inhibitory coupling coefficient (Sanz-Leon et al
Inhibitory to inhibitory coupling coefficient (Sanz-Leon et al., 2015)
Maximum value of the excitatory response function
Maximum value of the inhibitory response function
In order to have resting state (E=0 and I=0) in absence of external input, the logistic curve are translated downward S(0)=0
Excitatory population, membrane time-constant (Wilson and Cowan, 1972)
Inhibitory population, membrane time-constant (Wilson and Cowan, 1972)
Excitation threshold of excitatory population (Sanz-Leon et al
Excitation threshold of excitatory population (Sanz-Leon et al., 2015).
Excitation threshold of inhibitory population (Sanz-Leon et al
Excitation threshold of inhibitory population (Sanz-Leon et al., 2015).
State-variable of the Wilson and Cowan model (Wilson and Cowan, 1973), denoting the mean firing rate of all excitatory neurons of the population
State-variable of the Wilson and Cowan model (Wilson and Cowan, 1973), denoting the mean firing rate of all inhibitory neurons of the population
Fe_ext (ZerlautAdaptationFirstOrder derived variable)
Piecewise((0, K_ext_e*(c_pop0 + lc_E + ou_drift*weight_noise) < 0), (c_pop0 + lc_E + ou_drift*weight_noise, True))
Fi_ext (ZerlautAdaptationFirstOrder derived variable)
S_e (ZerlautAdaptationFirstOrder derived variable)
Scaling of the remote input for the inhibitory population with respect to the excitatory population.
S_i (ZerlautAdaptationFirstOrder derived variable)
T_V_e (ZerlautAdaptationFirstOrder derived variable)
T_V_i (ZerlautAdaptationFirstOrder derived variable)
T_e (ZerlautAdaptationFirstOrder derived variable)
T_i (ZerlautAdaptationFirstOrder derived variable)
T_m_e (ZerlautAdaptationFirstOrder derived variable)
T_m_i (ZerlautAdaptationFirstOrder derived variable)
U_e_e (ZerlautAdaptationFirstOrder derived variable)
U_e_i (ZerlautAdaptationFirstOrder derived variable)
U_i_e (ZerlautAdaptationFirstOrder derived variable)
U_i_i (ZerlautAdaptationFirstOrder derived variable)
V_e (ZerlautAdaptationFirstOrder derived variable)
V_i (ZerlautAdaptationFirstOrder derived variable)
V_thre_e (ZerlautAdaptationFirstOrder derived variable)
V_thre_i (ZerlautAdaptationFirstOrder derived variable)
f_out_e (ZerlautAdaptationFirstOrder derived variable)
erfc(sqrt(2)*(V_thre_e - mu_V_e)/(2*sigma_V_e))/(2*T_V_e)
f_out_i (ZerlautAdaptationFirstOrder derived variable)
erfc(sqrt(2)*(V_thre_i - mu_V_i)/(2*sigma_V_i))/(2*T_V_i)
fe_e (ZerlautAdaptationFirstOrder derived variable)
K_ext_e*(Fe_ext + external_input_ex_ex) + N_tot*p_connect_e*(1.0 - g)*(E + 1/1000000)
fe_i (ZerlautAdaptationFirstOrder derived variable)
K_ext_e*(Fe_ext + external_input_in_ex) + N_tot*p_connect_e*(1.0 - g)*(E + 1/1000000)
fi_e (ZerlautAdaptationFirstOrder derived variable)
K_ext_i*(Fi_ext + external_input_ex_in) + N_tot*g*p_connect_i*(I + 1/1000000)
fi_i (ZerlautAdaptationFirstOrder derived variable)
K_ext_i*(Fi_ext + external_input_in_in) + N_tot*g*p_connect_i*(I + 1/1000000)
lc_E (ZerlautAdaptationFirstOrder derived variable)
lc_I (ZerlautAdaptationFirstOrder derived variable)
mu_G_e (ZerlautAdaptationFirstOrder derived variable)
mu_G_i (ZerlautAdaptationFirstOrder derived variable)
mu_Ge_e (ZerlautAdaptationFirstOrder derived variable)
mu_Ge_i (ZerlautAdaptationFirstOrder derived variable)
mu_Gi_e (ZerlautAdaptationFirstOrder derived variable)
mu_Gi_i (ZerlautAdaptationFirstOrder derived variable)
mu_V_e (ZerlautAdaptationFirstOrder derived variable)
(E_L_e*g_L + E_e*mu_Ge_e + E_i*mu_Gi_e - W_e)/mu_G_e
mu_V_i (ZerlautAdaptationFirstOrder derived variable)
(E_L_i*g_L + E_e*mu_Ge_i + E_i*mu_Gi_i - W_i)/mu_G_i
sigma_V_e (ZerlautAdaptationFirstOrder derived variable)
sigma_V_i (ZerlautAdaptationFirstOrder derived variable)
C_m (ZerlautAdaptationFirstOrder parameter)
Normalization factors page 48 after the equation 4 from [ZD_2018]
DTvN0 (ZerlautAdaptationFirstOrder parameter)
DmuV0 (ZerlautAdaptationFirstOrder parameter)
DsV0 (ZerlautAdaptationFirstOrder parameter)
leak reversal potential for excitatory [mV]
E_L_e (ZerlautAdaptationFirstOrder parameter)
leak reversal potential for inhibitory [mV]
E_L_i (ZerlautAdaptationFirstOrder parameter)
E_e (ZerlautAdaptationFirstOrder parameter)
E_i (ZerlautAdaptationFirstOrder parameter)
Number of excitatory connexions from external population
K_ext_e (ZerlautAdaptationFirstOrder parameter)
Number of inhibitory connexions from external population
K_ext_i (ZerlautAdaptationFirstOrder parameter)
N_tot (ZerlautAdaptationFirstOrder parameter)
Polynome of excitatory phenomenological threshold (order 9)
P0_e (ZerlautAdaptationFirstOrder parameter)
Polynome of inhibitory phenomenological threshold (order 9)
P0_i (ZerlautAdaptationFirstOrder parameter)
P1_e (ZerlautAdaptationFirstOrder parameter)
P1_i (ZerlautAdaptationFirstOrder parameter)
P2_e (ZerlautAdaptationFirstOrder parameter)
P2_i (ZerlautAdaptationFirstOrder parameter)
P3_e (ZerlautAdaptationFirstOrder parameter)
P3_i (ZerlautAdaptationFirstOrder parameter)
P4_e (ZerlautAdaptationFirstOrder parameter)
P4_i (ZerlautAdaptationFirstOrder parameter)
P5_e (ZerlautAdaptationFirstOrder parameter)
P5_i (ZerlautAdaptationFirstOrder parameter)
P6_e (ZerlautAdaptationFirstOrder parameter)
P6_i (ZerlautAdaptationFirstOrder parameter)
P7_e (ZerlautAdaptationFirstOrder parameter)
P7_i (ZerlautAdaptationFirstOrder parameter)
P8_e (ZerlautAdaptationFirstOrder parameter)
P8_i (ZerlautAdaptationFirstOrder parameter)
P9_e (ZerlautAdaptationFirstOrder parameter)
P9_i (ZerlautAdaptationFirstOrder parameter)
P_e (ZerlautAdaptationFirstOrder parameter)
P_i (ZerlautAdaptationFirstOrder parameter)
Q_e (ZerlautAdaptationFirstOrder parameter)
Q_i (ZerlautAdaptationFirstOrder parameter)
S_i (ZerlautAdaptationFirstOrder parameter)
Time scale of describing network activity
T (ZerlautAdaptationFirstOrder parameter)
TvN0 (ZerlautAdaptationFirstOrder parameter)
a_e (ZerlautAdaptationFirstOrder parameter)
a_i (ZerlautAdaptationFirstOrder parameter)
Excitatory adaptation current increment [pA]
b_e (ZerlautAdaptationFirstOrder parameter)
Inhibitory adaptation current increment [pA]
b_i (ZerlautAdaptationFirstOrder parameter)
external_input_ex_ex (ZerlautAdaptationFirstOrder parameter)
external_input_ex_in (ZerlautAdaptationFirstOrder parameter)
external_input_in_ex (ZerlautAdaptationFirstOrder parameter)
external_input_in_in (ZerlautAdaptationFirstOrder parameter)
g (ZerlautAdaptationFirstOrder parameter)
g_L (ZerlautAdaptationFirstOrder parameter)
muV0 (ZerlautAdaptationFirstOrder parameter)
p_connect_e (ZerlautAdaptationFirstOrder parameter)
p_connect_i (ZerlautAdaptationFirstOrder parameter)
sV0 (ZerlautAdaptationFirstOrder parameter)
tau_OU (ZerlautAdaptationFirstOrder parameter)
tau_e (ZerlautAdaptationFirstOrder parameter)
tau_i (ZerlautAdaptationFirstOrder parameter)
Adaptation time constant of excitatory neurons [ms]
tau_w_e (ZerlautAdaptationFirstOrder parameter)
Adaptation time constant of inhibitory neurons [ms]
tau_w_i (ZerlautAdaptationFirstOrder parameter)
weight_noise (ZerlautAdaptationFirstOrder parameter)
Firing rate of excitatory population in KHz
E (ZerlautAdaptationFirstOrder state variable)
Firing rate of inhibitory population in KHz
I (ZerlautAdaptationFirstOrder state variable)
W_e (ZerlautAdaptationFirstOrder state variable)
E*b_e - W_e/tau_w_e + a_e*(-E_L_e + mu_V_e)/tau_w_e
W_i (ZerlautAdaptationFirstOrder state variable)
I*b_i - W_i/tau_w_i + a_i*(-E_L_i + mu_V_i)/tau_w_i
ou_drift (ZerlautAdaptationFirstOrder state variable)
coupled_input (ZetterbergJansen derived variable)
2*e0/(exp(rho_1*(-c_pop0 - local_coupling*v6 + rho_2)) + 1)
sigma_v1 (ZetterbergJansen derived variable)
sigma_v23 (ZetterbergJansen derived variable)
sigma_v45 (ZetterbergJansen derived variable)
Maximum firing rate to the pyramidal population [ms^-1]
Maximum firing rate to the pyramidal population [ms^-1]. (External stimulus. Constant intensity.Entry point for coupling.)
Maximum firing rate to the interneurons population [ms^-1]
Maximum firing rate to the interneurons population [ms^-1]. (External stimulus. Constant intensity.Entry point for coupling.)
Maximum firing rate to the stellate population [ms^-1]
Maximum firing rate to the stellate population [ms^-1]. (External stimulus. Constant intensity.Entry point for coupling.)
Half of the maximum population mean firing rate [ms^-1]
Average number of synapses between populations (pyramidal to stellate)
Coupling factor from the extrinisic input to the spiny stellate population
Average number of synapses between populations (stellate to pyramidal)
Coupling factor from the extrinisic input to the pyramidal population
Connectivity constant (pyramidal to interneurons)
Coupling factor from the extrinisic input to the inhibitory population
Connectivity constant (interneurons to pyramidal)
Connectivity constant (interneurons to interneurons)
Reciprocal of the time constant of passive membrane and all other spatially distributed delays in the dendritic network [ms^-1]
Reciprocal of the time constant of passive membrane and all other spatially distributed delays in the dendritic network [ms^-1]. Also called average synaptic time constant.
Steepness of the sigmoidal transformation [mV^-1]
Firing threshold (PSP) for which a 50% firing rate is achieved
Firing threshold (PSP) for which a 50% firing rate is achieved. In other words, it is the value of the average membrane potential corresponding to the inflection point of the sigmoid [mV]. Population mean firing threshold.
He*ke*(gamma_1*sigma_v23 + gamma_1T*(U + coupled_input)) - ke**2*v1 - 2*ke*y1
He*ke*(gamma_2*sigma_v1 + gamma_2T*(P + coupled_input)) - ke**2*v2 - 2*ke*y2
Hi*gamma_4*ki*sigma_v45 - ki**2*v3 - 2*ki*y3
He*ke*(gamma_3*sigma_v23 + gamma_3T*(Q + coupled_input)) - ke**2*v4 - 2*ke*y4
Hi*gamma_5*ki*sigma_v45 - ke**2*v5 - 2*ki*y5
Continue the equilibrium branch of the Generic2dOscillator as parameter a is varied. Detect Hopf bifurcations and continue periodic orbits from all Hopf points.
Comprehensive bifurcation analysis of the Generic2dOscillator. Continues the equilibrium branch as parameter a is varied, detects fold and Hopf bifurcations, then branches to periodic orbits from both Hopf points with fine-tuned solver settings.
Nonlinear 6th-generation MNI152 average, 2009b release (symmetric variant). Produced by the McConnell Brain Imaging Centre (Fonov et al., 2011).
Nonlinear 6th-generation MNI152 average, 2009c release (symmetric variant). Produced by the McConnell Brain Imaging Centre (Fonov et al., 2011).
Nonlinear 6th-generation MNI152 average, 2009c release, asymmetric variant (Fonov et al., 2011). Default template space for fMRIPrep and many modern BIDS pipelines. TemplateFlow id: `tpl-MNI152NLin2009cAsym`.
Sixth-generation nonlinear ICBM152 average (Mazziotta et al., 2001), asymmetric variant. This is the template shipped with FSL as `MNI152_T1_*mm.nii.gz` (commonly referred to as "FSLMNI152" or generic "MNI152" in older literature). TemplateFlow id: `tpl-MNI152NLin6Asym`.
Instantaneous linear coupling using vectorized matrix multiplication. Applies global coupling strength G to the weighted sum of source states. Faster than element-wise LinearCoupling for dense all-to-all connectivity.
Global coupling strength scaling long-range connections
Midpoint of the linear portion of the sigmoid
Linear scaling factor of the coupled (delayed) input.
Shifts the base of the connection strength while maintaining the absolute difference between different values.
The steepness of the sigmoidal transformation
Fitting functional connectivity using the two-population Reduced Wong-Wang model with explicit excitatory and inhibitory populations. Combines Feedback Inhibition Control (FIC) to maintain local E-I balance with EIB tuning to globally optimize network connectivity. Supports both iterative (FIC+EIB) and gradient-based optimization approaches.
EI Tuning: Functional Connectivity Fitting
Reproducing MEG Resting-State Frequency Gradients with Network Dynamics. This experiment fits region-specific Jansen-Rit parameters to reproduce the empirical frequency gradient from visual cortex (11 Hz) to association areas (7 Hz).
Fitting functional connectivity using the Reduced Wong-Wang model. Two-stage optimization: global parameters first, then regional heterogeneity. Based on tvboptim RWW.qmd tutorial.
Multiscale brain network model coupling large-scale structural connectivity with a small-scale frontoparietal circuit for decision-making (DM) and working memory (WM). The large-scale model uses the Reduced Wong-Wang model (Deco et al., 2014, JNeurosci) with explicit excitatory and inhibitory populations. The functional circuit implements winner-take-all DM and persistent-activity WM based on Murray, Jaramillo & Wang (2017, JNeurosci). Key innovation: The ratio of long-range excitation (LRE) to feedforward inhibition (FFI) controls functional connectivity between brain regions. This E/I balance tuning enables fitting personalized brain network models to empirical functional connectivity and explains individual differences in cognitive performance (speed-accuracy trade-off in intelligence tests).
Multiscale BNM: Decision-Making with E/I Balance Tuning
65-channel EEG sensor array (Brainstorm default). Positions in mm, Brainstorm coordinate space.
276-channel MEG sensor array (Brainstorm default). Positions and orientations in mm.
588-contact stereo-EEG array (TVB default). Intracranial depth electrodes. No precomputed projection — gain is computed analytically.
Example network with 3 nodes and two different coupling types (excitatory and inhibitory)
MNE standard_1005 EEG sensors (MNI, mm) with fsaverage aparc projection gain matrix (sensors x regions).
Schaefer2018 100-parcel 7Networks multi-graph network from Hansen et al. (2022). Contains structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state fMRI, both derived from the HCP Young Adult cohort. SC edge weights are log-transformed streamline counts normalized to [0, 1]. FC edge weights are Pearson correlations.
Lobar8 surface (fsaverage 164k, 327684 vertices)
Lobar8 LH surface (fsaverage 164k, 163842 vertices)
Lobar8 RH surface (fsaverage 164k, 163842 vertices)
Lobar surface (fsLR 32k, 64984 vertices)
conduction_speed (SmileyNetwork parameter)
conduction_speed (acq-EEGBrainstorm65_sensors parameter)
conduction_speed (acq-MEGBrainstorm276_sensors parameter)
conduction_speed (acq-SEEG588_sensors parameter)
conduction_speed (example_3node_network parameter)
conduction_speed (sensors_eeg_standard1005_fsaverage_aparc_projection parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-Hansen2022_atlas-Schaefer2018_seg-7Networks_scale-100_desc-SCFC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-100_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-200_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-300_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-400_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-500_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-600_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-700_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-800_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-17Networks_scale-900_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-100_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-200_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-300_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-400_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-500_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-600_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-700_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-800_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-HCPYA_rec-dTOR_atlas-Schaefer2018_seg-7Networks_scale-900_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-Schaefer2018_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-Schaefer2018_seg-17Networks_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-PPMI85_rec-PPMI85_atlas-Schaefer2018_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-FSLMNI152_cohort-PPMI85_rec-PPMI85_atlas-Schaefer2018_seg-17Networks_scale-1000_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-HCPYA_rec-dTOR_atlas-HCPMMP1_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-HCPYA_rec-dTOR_atlas-HCPMMP1_seg-ordered_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-HCPMMP1_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-HCPMMP1_seg-ordered_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-PPMI85_rec-PPMI85_atlas-HCPMMP1_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009bAsym_cohort-PPMI85_rec-PPMI85_atlas-HCPMMP1_seg-ordered_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_atlas-Lobar8_desc-surf_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_atlas-Lobar8_hemi-L_desc-surf_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_atlas-Lobar8_hemi-R_desc-surf_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-DesikanKilliany_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-Destrieux_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-HCPex_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-Lobar8_desc-SCFC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-Lobar_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-Lobar_desc-surf_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-Yeo17_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-HCPYA_rec-dTOR_atlas-virtualdbs_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-DesikanKilliany_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-Destrieux_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-HOCPA_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-Yeo17_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-MghUscHcp32_rec-MghUscHcp32_atlas-virtualdbs_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-PPMI85_rec-PPMI85_atlas-DesikanKilliany_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-PPMI85_rec-PPMI85_atlas-Destrieux_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-PPMI85_rec-PPMI85_atlas-Yeo17_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_cohort-PPMI85_rec-PPMI85_atlas-virtualdbs_desc-SC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_rec-avgMatrix_atlas-DesikanKilliany_desc-SCFC_relmat parameter)
conduction_speed (tpl-MNI152NLin2009cAsym_rec-avgMatrix_atlas-Lobar8_desc-SCFC_relmat parameter)
conduction_speed (tpl-fsaverage_acq-EEGstandard1005_atlas-DesikanKilliany_desc-projection_sensors parameter)
Functional Connectivity (FC) observation model that computes pairwise Pearson correlation coefficients between regional time series. Commonly used to characterize the statistical dependencies between brain regions from fMRI or simulated neural activity.
Records the afferent (incoming) coupling term at each network node at every integration step. Records the coupling input that drives the dynamics rather than the model state itself.
Temporally averaged afferent coupling input at each node. Combines coupling input recording with temporal averaging.
BOLD fMRI observation model using a Double Exponential hemodynamic response function.
BOLD fMRI observation model using a Gamma-shaped hemodynamic response function. Provides a simpler, unimodal HRF shape.
BOLD fMRI observation model using the canonical double-gamma hemodynamic response function (Glover 1999). Standard HRF used in most fMRI analysis packages (SPM, FSL, AFNI).
BOLD fMRI observation model that computes the hemodynamic response at each source location and spatially averages within brain regions using a parcellation mapping. Produces per-region BOLD signals suitable for comparison with parcellated empirical data.
BOLD fMRI observation model using the Balloon-Windkessel hemodynamic model. Transforms neural activity into simulated BOLD signal via convolution with a hemodynamic response function (First Order Volterra kernel), followed by a nonlinear Volterra transformation.
Forward solution for scalp electroencephalography. Projects source neural activity through a lead field (gain) matrix to electrode locations on the scalp. If no precomputed gain is available, uses a single-sphere analytic approximation (Sarvas 1987, Eq. 12). Supports re-referencing: common average, single-electrode, or ideal reference-free recording.
Spatial mean across all network nodes at each sampling period. Reduces spatial dimension to a single global signal per state variable.
Forward solution for intracranial/stereoelectroencephalography (SEEG). Projects source activity through a lead field matrix to implanted depth electrode contacts. Uses the same single-sphere analytic formula as scalp EEG (Sarvas 1987, Eq. 12), but with electrode positions inside the brain volume.
Forward solution for magnetoencephalography. Projects source neural activity through a lead field matrix to MEG sensor locations using oriented gradiometers. If no precomputed gain is available, uses the single-sphere analytic formula (Sarvas 1987, Eq. 25) for the magnetic field produced by current dipoles in a conducting sphere.
Records all state variables at every integration step without any transformation. Identity passthrough of the full simulation state.
Computes spatially averaged signals within regions defined by a spatial mask (node-to-region assignment). The mask assigns each node to a region index, and the output is the mean signal per region.
Temporal decimation of selected state variables. Records every N-th integration step without any averaging or smoothing.
Running temporal mean over a sliding window of configurable length. Returns one averaged sample per period.
Apply Fisher z-transformation to correlations
Volume conductor conductivity for the single-sphere analytic approximation. Only used when no precomputed gain is available.
Re-referencing scheme. Options: - Electrode label (e.g. "Cz"): subtract that electrode signal - "average": subtract mean across all channels - null: ideal reference-free recording
Tissue conductivity for the analytic point-dipole approximation. Only used when no precomputed gain is available.
Magnetic constant (permeability of free space)
Vector of length N_nodes mapping each node to a region index (0-indexed). Inferred from network topology if not provided.
spatial_mask (spatial_average parameter)
A software package for continuation and bifurcation problems in ordinary differential equations. The standard tool for numerical continuation in dynamical systems since 1980.
A high-performance library for computational neuroscience simulation with native support for morphologically detailed neuron models on modern hardware including GPUs and many-core CPUs.
Brain Scaffold Builder — a framework for reconstructing and simulating data-driven brain tissue models using morphological and connectivity scaffolds.
A Julia package for bifurcation analysis of large-dimensional equations. Supports continuation of equilibria, periodic orbits, and detection of bifurcation points for ODE and PDE systems.
Blue Brain Python Optimisation Library — a framework for data-driven model parameter optimisation using multi-objective evolutionary algorithms on NEURON models.
A set of JavaScript visualization tools for rendering brain surfaces, volumes, and connectivity data in the browser using WebGL.
A flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation built on JAX. Supports spiking, rate-based, and mean-field models at all scales.
A neural simulator exploring AGI-inspired approaches to brain simulation, with a focus on large-scale spiking neuron models and real-time interaction.
A collaborative, open-source application for MEG/EEG/sEEG/ECoG/fNIRS data analysis, visualization, and source localization.
A simulator for spiking neural networks. Equations are specified in standard mathematical notation, and Brian2 automatically generates efficient C++ code for numerical integration.
An efficient, easy-to-use, GPU-accelerated library for simulating large-scale spiking neural network models with conductance-based synapses, STDP, and neuromodulation.
Optimized compute engine for the NEURON simulator. Provides efficient execution of NEURON models on CPUs and GPUs using NMODL code generation.
A simulation framework for cortical microcircuit models with support for data-driven customization and modular circuit specification. Uses Brian2 as its simulation backend.
A suite for numerically solving differential equations in Julia. Supports ODEs, SDEs, DAEs, DDEs, and PDEs with a unified interface, high performance, and extensive algorithm selection.
An interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data using independent component analysis, time/frequency analysis, and other methods.
Electrophysiology Analysis Toolkit — a Python library for the analysis of electrophysiological data, including spike-train statistics, spectral analysis, and unitary event analysis.
A fast C++ implementation of the Dynamic Mean Field (DMF) model for whole-brain simulation, with Python bindings. Optimized for rapid parameter exploration on connectome data.
A MATLAB toolbox for MEG, EEG, iEEG and NIRS analysis, including preprocessing, source reconstruction, statistical testing, and connectivity analysis.
GEneral NEural SImulation System — one of the earliest simulators for biologically realistic modeling of neural systems, from sub-cellular components to large networks.
GPU Enhanced Neuronal Networks — a framework for GPU-accelerated spiking neural network simulations using NVIDIA CUDA. Generates optimized GPU kernels from model descriptions.
Human Neocortical Neurosolver — a tool for simulating the cellular and network-level origin of MEG/EEG signals using biophysically detailed neocortical column models on NEURON.
Low Entropy Model Specification — an XML-based language for defining the structure and dynamics of hierarchically structured models of biological systems. Foundation for NeuroML2.
A Python module for calculating local field potentials (LFPs) and extracellular potentials from multicompartment neuron models using the line-source method on NEURON simulations.
Monte Carlo Cell — a program for realistic simulation of cellular signaling using spatially realistic 3D models of sub-cellular reaction-diffusion dynamics.
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.
Multiscale Object-Oriented Simulation Environment. Supports models from sub-cellular signaling to large networks, including reaction-diffusion, electrical, and chemical compartmental models.
A MATLAB continuation toolbox for interactive bifurcation analysis of dynamical systems. Computes equilibria, limit cycles, and bifurcation curves with graphical user interface.
A curated database of published computational neuroscience models. Provides source code and metadata for thousands of models linked to peer-reviewed publications.
NEST is a simulator for spiking neural network models that focuses on the dynamics, size, and structure of neural systems rather than on the exact morphology of individual neurons.
NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for constructing, managing, and using biophysically realistic models with active membranes.
Neurodata Without Borders — a data standard for neurophysiology providing a common format for storing diverse cellular and systems neuroscience data in HDF5 files.
A Python library for building and simulating large-scale brain models using the Neural Engineering Framework (NEF). Supports spiking and rate-based neurons with backends including NumPy, TensorFlow (NengoDL), and neuromorphic hardware (NengoLoihi).
A Python package for representing electrophysiology data in a common object model independently of the data source. Core I/O library for the NeuralEnsemble ecosystem.
A high-level Python interface for NEURON that facilitates the development, simulation, and analysis of biological neuronal networks. Supports multiscale models from detailed to abstract.
A Julia package for simulating dynamics on networks. Provides an efficient interface for defining node and edge dynamics that compiles to optimized DifferentialEquations.jl ODE/SDE problems.
An XML-based model description language for computational neuroscience. Provides a standardized format for defining models of neurons, synapses, networks, and channels that can be simulated across multiple platforms.
A Julia package for building brain circuit models from composable neural mass and spiking "blox" components. Built on ModelingToolkit.jl for symbolic model construction and automatic differentiation.
A parameter optimization tool for neuroscience models. Uses evolutionary algorithms to fit model parameters to experimental electrophysiology data.
A language for describing the dynamics and connectivity of spiking neuronal network models. Designed by the INCF to enable simulator-independent model exchange.
A platform for sharing and collaboratively developing computational neuroscience models in standardized formats, primarily NeuroML.
An open science project to build a comprehensive computational model of the C. elegans nematode, including its 302-neuron nervous system.
Parallel Stochastic Ion Channel Simulator — a fast simulator for stochastic ion channel models in morphologically detailed neurons using efficient population-based algorithms.
A Python package for simulator-independent specification of spiking neuronal network models. Provides a common API for NEST, NEURON, Brian2, GeNN, and SpiNNaker backends.
A Python framework for rate-based and neural mass network simulations. Provides symbolic equation parsing, automatic code generation, and parameter continuation support.
A Python module for fitting, simulating, and analyzing optogenetic rhodopsin photocurrent models. Integrates with NEURON and Brian2.
Scalable Open Network Architecture ToolKit and Application — a data format specification for storing large-scale neural network models and simulation results. Joint Allen Institute / Blue Brain standard.
Statistical Parametric Mapping — software for the analysis of brain imaging data sequences (fMRI, PET, SPECT, EEG, MEG). Standard tool for voxel-based and DCM analyses.
STochastic Engine for Pathway Simulation — a stochastic reaction-diffusion simulator for modeling biochemical signaling pathways in realistic 3D cellular morphologies.
A Python tool for creating detailed neuronal network models with realistic 3D neuron placement, morphology, and connectivity using touch detection. Exports to NEURON via SONATA format.
A unified framework for spike sorting, providing a common interface to many spike sorting algorithms, preprocessing tools, and quality metrics.
A graphical tool for building neural network models using SpineML component-based descriptions, with export to multiple simulation engines.
A declarative XML-based language for describing spiking neural network models using a component-based approach. Designed for simulator independence.
The Virtual Brain — a neuroinformatics platform for full-brain network simulation using neural mass and mean-field models on individual connectome data.
The Virtual Brain Ontology — a Python library for knowledge representation, code generation, and simulation of large-scale brain network models. Integrates a LinkML-based ontology with JAX-accelerated numerical backends and interoperates with TVB, PyRates, NEURON, NEST, NeuroML, and NetworkDynamics.jl.
Differentiable whole-brain simulation and optimization framework using JAX. Supports gradient-based fitting of neural mass models to empirical neuroimaging data.
Java-based toolchain for NeuroML/LEMS model validation, simulation, and export to multiple simulator backends including NEURON, NEST, and Brian2.
A differentiable simulator for biophysical neuron models built on JAX. Supports multi-compartment Hodgkin-Huxley models with automatic differentiation for gradient-based parameter inference.
A graphical tool for constructing and simulating biologically realistic 3D neuronal networks with morphologically detailed cells. Exports to NEURON, GENESIS, and NeuroML.
A simulation framework for whole-brain neural mass models with built-in exploration, optimization, and fMRI/EEG forward-model support on empirical connectomes.
Python API and command-line tools for NeuroML/LEMS model creation, validation, visualization, and simulation via jNeuroML and direct simulator backends.
PyNN interface for the GeNN GPU simulator, enabling PyNN models to run on NVIDIA GPUs via the GeNN backend.
Software stack enabling PyNN models to execute on the SpiNNaker neuromorphic computing platform. Translates PyNN network descriptions into configurations for SpiNNaker's many-core ARM processors.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Chaotic Dynamics in a Quartic Nuclear Collective Hamiltonian
International Journal of Modern Physics E
A hyperchaotic attractor coined from chaotic L"u system
Role of local network oscillations in resting-state functional connectivity
International Journal of Nonlinear Science
Reference for network mean-field neural mass model
Bursting types and stable domains of Rulkov neuron network with mean field coupling
International Journal of Bifurcation and Chaos
Generating hyperchaotic L"u attractor via state feedback control
Physica A: Statistical Mechanics and its Applications
Research concerning the theory of nonlinear resonance
Archiv f"ur Elektronik und "Ubertragungstechnik
The brain resting state: dynamics and models
Abrupt Phase-Space Contraction in a Magnetotransport System
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations
How local excitation--inhibition ratio impacts the whole brain dynamics
The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core
Erzwungene Schwingungen bei ver"anderlicher Eigenfrequenz und ihre technische Bedeutung
A reduced model of coupled neural masses with complex dynamics
Nuclear Theory: Nuclear Models Collective and Single-Particle Phenomena
Mathematical Foundations of Neuroscience
Quantitative Universality for a Class of Nonlinear Transformations
Impulses and physiological states in theoretical models of nerve membrane
Multistability, phase diagrams, and intransitivity in the Lorenz-84 low-order atmospheric circulation model
Optimal time scale for spike-time reliability: theory, simulations, and experiments
A mean-field description of bursting dynamics in neural populations
A New Deterministic Model of Chaotic Reversals
Final state sensitivity: An obstruction to predictability
A Method for Determining a Stochastic Transition
Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields
The Applicability of the Third Integral of Motion: Some Numerical Experiments
A Two-Dimensional Mapping with a Strange Attractor
A model of neuronal bursting using three coupled first order differential equations
Proceedings of the Royal Society of London. Series B, Biological Sciences
A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve
Neural networks and physical systems with emergent collective computational abilities
Proceedings of the National Academy of Sciences
Neurons with graded response have collective computational properties like those of two-state neurons
Fundamental Unpredictability in Multispecies Competition
Construction of dynamical non-linear components based on Lorenz system and symmetric group of permutations
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting
Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns
Parameter exploration of a realistic neural mass model of visual cortex
On a Robust Method to Detect Intermittency
Journal of Physics A: Mathematical and General
Self-entrainment of a Population of Coupled Nonlinear Oscillators
International Symposium on Mathematical Problems in Theoretical Physics
A novel four-wing strange attractor born in bistability
Intermittency, self-similarity and 1/f spectrum in dissipative dynamical systems
Simple mathematical models with very complicated dynamics
Long-range correlations in intermittent maps
Chaoticity Evolution in a Quartic Collective Hamiltonian
Dragon-king-like extreme events in coupled bursting neurons
Macroscopic description for networks of spiking neurons
Voltage oscillations in the barnacle giant muscle fiber
An active pulse transmission line simulating nerve axon
The transition to chaotic attractors with riddled basins
A new hyperchaotic system from the L"u system and its control
Journal of Computational and Applied Mathematics
A neural mass model of seizure initiation and propagation
Individual structural connectivity defines propagation networks in partial epilepsy
Proceedings of the Cambridge Philosophical Society
Regularization of Synchronized Chaotic Bursts
Modeling of spiking-bursting neural behavior using two-dimensional map
Chaotic Modelling and Simulation: Analysis of Chaotic Models, Attractors and Forms
Elegant Chaos: Algebraically Simple Chaotic Flows
Simplest Chaotic Flows with Involutional Symmetries
Classifying and quantifying basins of attraction
Chaos: An Interdisciplinary Journal of Nonlinear Science
Hyperchaotic dynamics in a R"ossler-like system
Thermohaline convection with two stable regimes of flow
From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering
Deterministic chaos seen in terms of feedback circuits: Analysis, synthesis, \& biological applications
An Experimental Approach to Nonlinear Dynamics and Chaos
Randomly Transitional Phenomena in the System Governed by Duffing's Equation
Biophysical ingredients for a robust model of cortical dynamics
A hyperchaos generated from Lorenz system
A 3-D four-wing attractor and its analysis
Excitatory and inhibitory interactions in localized populations of model neurons
A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
The simplest case of a strange attractor
A mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons
A model for the interrelationship between EEG and evoked potentials
The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science
Slot for the abbreviation of a resource.
Enter any alternate names, including abbreviations, for this entity.
Add the axes orientation in standard anatomical terms (XYZ).
Enter the origin (central point where all axes intersect).
Add the color code used for visual representation of this entity.
Add the location of the data file containing the parcellation terminology.
Add all image files used as visual representation of this common coordinate space version.
Linked type for the version of a brain atlas or coordinate space.
Linked type for the license of the brain atlas or coordinate space version.
Add the native unit that is used for this common coordinate space version.
Enter the internationalized resource identifier (IRI) to the related ontological terms.
Add the related anatomical entity as defined by the UBERON ontology.
Enter the version identifier of this brain atlas or coordinate space version.
Bistable
Bursting
Chaotic
Aggregate input arriving at a node from connected nodes; modelled as the postsynaptic potential induced by long-range projections.
Specification of a coupling input channel for multi-coupling dynamics
CouplingInput
Deterministic
Excitable
Fixed point
Homoclinic
Hopf
Limit cycle
Multistable
Oscillatory
Quiescent
Saddle-node
Strange attractor
Torus
Hardware accelerator: 'cpu', 'gpu', 'tpu'
Parameter symbols modifiable in the affect function.
State variable symbols modifiable in the affect function.
Averaging mode across trials. 'trials' = average over n_trials independent runs (evoked potential paradigm). None = return all trials.
Path to BEP017-compliant BIDS directory for loading connectivity matrices
BIDS cohort- entity (e.g., HCPYA, PPMI85)
Parameter symbols accessible in the condition function.
State variable symbols accessible in the condition function. For edges, can include source/destination vertex outputs.
Reference to connectivity data (_conndata-network_connectivity.tsv).
Path to companion data file. Supported extensions: .h5 (HDF5), .zarr/ (Zarr), .csv (legacy single-matrix). Null if no companion data needed.
Dataset path for array-valued parameters. When set, the parameter value is stored in the binary companion file (HDF5 or Zarr) at this path. The value slot is omitted.
Short alphanumeric identifier for the BIDS desc- filename entity (e.g., SC, FC, EC, SCFC). Classifies the connectivity modality of the network's edge measures.
Ordered labels for the matrix columns (dim-1) when the matrix is non-square. Row labels (dim-0) are the parent Network's node labels. Stored as HDF5 dimension scales in the companion file.
Connectivity (indices) or file reference to topology.
Type of equation: 'differential' (default) means dx/dt = rhs, 'algebraic' means 0 = rhs or x ~ rhs (DAE constraint). Algebraic equations are used by ModelingToolkit.jl backend.
Software/agent identifier (prov:wasGeneratedBy)
Optional file for geometry/ROI mask (e.g., NIfTI, GIfTI).
BIDS hemi- entity (L or R) for hemisphere-specific surface/volume data
Named keys for multi-dimensional coupling. When dimension > 1, provides symbolic names for each index (e.g., keys: [lre, ffi] for dimension: 2). Used in equations as variable names.
Mapping of dimension names to their labels (JSON-encoded dict).
Ordering of dimensions: Time, State Variable, Space, Mode.
Learning rate schedule: 'constant', 'linear', 'exponential'
Path to external mesh file (GIFTI, VTK, MSH, FreeSurfer, etc.).
Explicit format override (gifti, freesurfer, meshio, vtk, gmsh). Auto-detected from extension if null.
Combination mode: 'product' (full grid), 'zip' (paired)
BIDS ModelEq reference: path to _eq.xml LEMS file.
BIDS ModelParam reference: path to _param.xml LEMS file.
Globally unique identifier for the entity.
Optional step label; used in pipelines to key step outputs.
HDF5 dataset path for node-to-parent mapping. Int32 array of shape (N,) where entry i is the parent node ID. Required when parent_network is set.
BEP017 measure names for observational targets (e.g., BoldCorrelation)
Output symbol names for the edge model. E.g. ['P'] for a scalar power flow, ['Fx', 'Fy'] for 2D forces. Maps directly to outsym in ND.jl EdgeModel. If not specified, derived from coupling variables of the connected vertex dynamics.
Path/URI to parent (coarser) Network. When set, this network is a refinement where each node maps to exactly one parent node via node_mapping.
Floating point precision: 'float32' or 'float64'
Processing pipeline used to generate the tractography
Publication or DOI reference for this tractography dataset
Unit of the sampling period (e.g., 'ms', 's').
BIDS seg- entity (e.g., ordered, ranked, 17Networks)
Name of a previously computed branch (for from_branch method).
Output variable from source node to use (e.g., 'x_out'). If not specified, uses first output variable from source dynamics.
BEP017 measure names for structural connectivity (e.g., streamlineCount, tractLength)
Edge symmetry type for NetworkDynamics.jl EdgeModel: 'directed' (default), 'antisymmetric', or 'symmetric'. AntiSymmetric edges flip sign for the reverse direction.
Component to attach this event to. Can be a node label, edge label, or 'all_edges'/'all_vertices' for broadcast. If not specified, event is experiment-level.
Path or name of the Network whose nodes define the columns of a non-square (projection) matrix. For example, a gain matrix with shape (n_sensors, n_regions) references the brain parcellation network here. Row labels come from the parent Network's own nodes.
Input variable on target node to connect to (e.g., 'c_in'). If not specified, uses first coupling input from target dynamics.
BIDS task name for the simulation (e.g., 'rest', 'simulation').
BIDS tpl- entity (e.g., FSLMNI152, MNI152NLin2009cAsym)
Physical unit of measurement. Values are drawn from the QUDT ontology (http://qudt.org/vocab/unit/) with UO cross-references where available.
Numeric, string, or boolean value. ScalarValue accepts any literal primitive type, allowing parameters to carry control flags (e.g., booleans) or symbolic placeholders alongside numeric defaults.
Who performed the rating (e.g., rater initials, clinician ID, or system)
Optional human-readable label (e.g., "1a")
Full name of the scale (e.g., Unified Parkinson’s Disease Rating Scale)
Full name of the score (e.g., Unified Parkinson's Disease Rating Scale - Part III)
PubMed ID, DOI, or other reference to the score definition
Timepoint of assessment (e.g., "1 month post-op", "6 months post-op")
High-level category (e.g., simulation, analysis, specification).
Original author(s) or creating organization(s).
Deterministic hash of the resolved dependency set.
Container image reference (e.g., ghcr.io/org/img:tag@sha256:...).
Digital Object Identifier for the software or its reference publication.
Funding bodies or grants (e.g., 'EU H2020 945539', 'NIH R01...').
Build or artifact hash for exact reproducibility.
Tags for topic-based discovery and clustering.
SPDX license identifier (e.g., MIT, GPL-3.0-only).
Current maintainer(s) or responsible organization(s).
(Deprecated) Use environment.requirements list instead.
Supported operating systems (e.g., Linux, macOS, Windows).
OS / architecture (e.g., linux-64, macos-arm64).
DOI of the primary reference publication for this tool.
Execution backends or platforms (e.g., MPI, OpenMP, CUDA, JAX).
Version or constraint specifier (e.g., '==2.7.3', '>=1.2,<2').
Path to BIDS dataset root directory. When set, subject networks and empirical data paths are resolved relative to this root.
Experimental condition label (e.g., 'rest', 'task-nback'). Maps to BIDS 'task-' entity.
Global condition labels applied across all subjects (e.g., ['rest', 'task-nback', 'task-motor']).
Paths to empirical recordings for this session (e.g., BOLD time series, MEG/EEG). Relative to dataset root.
Group assignment (e.g., 'control', 'patient', 'healthy'). Maps to participants.tsv 'group' column in BIDS.
Additional subject metadata as key-value pairs or path to a sidecar JSON file.
BIDS session identifier (without 'ses-' prefix). Examples: 'baseline', '6month', 'pre', 'post'.
BIDS-compatible subject identifier (without 'sub-' prefix). Examples: '01', 'ctrl03', 'patient17'.