Dynamics Database Gallery

Overview of all models in the TVBO database, organised by category.

Models from the TVBO database simulated for 100 ms and plotted by category. Each panel shows the first state variable; colours are sampled from the viridis colourmap.

Models that fail to integrate are skipped rather than reported as failures, so the gallery below is not necessarily complete. The summary table at the end gives the honest tally: Available models per category versus how many actually Plotted.

Code

import warnings, logging
warnings.filterwarnings("ignore")
logging.disable(logging.WARNING)

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from matplotlib.cm import ScalarMappable
from tvbo import Dynamics, SimulationExperiment

Code

DURATION = 100.0          # ms
CATEGORIES = [
    ("mean_field",        "Mean-Field Models"),
    ("neural_mass",       "Neural-Mass Models"),
    ("phase_oscillator",  "Phase-Oscillator Models"),
    ("phenomenological",  "Phenomenological Models"),
    ("spiking",           "Spiking / Single-Neuron Models"),
    ("generic",           "Generic Dynamical Systems"),
]

results = {}   # model_type -> [(name, data_array)]
for cat, _ in CATEGORIES:
    results[cat] = []
    for name in Dynamics.list_db(model_type=cat):
        try:
            d = Dynamics.from_db(name)
            exp = SimulationExperiment(dynamics=d)
            exp.integration.duration = DURATION
            r = exp.run()
            da = r.integration.data
            results[cat].append((name, da))
        except Exception:
            pass  # skip models that fail to run

Code

cmap = plt.get_cmap("viridis")

for cat, title in CATEGORIES:
    items = results[cat]
    if not items:
        continue
    n = len(items)
    ncols = min(6, n)
    nrows = int(np.ceil(n / ncols))
    fig, axes = plt.subplots(
        nrows, ncols,
        figsize=(ncols * 2.6, nrows * 1.8),
        squeeze=False,
    )
    fig.suptitle(title, fontsize=14, fontweight="bold", y=1.02)
    colors = [cmap(i / max(n - 1, 1)) for i in range(n)]

    for idx, (name, da) in enumerate(items):
        ax = axes[idx // ncols, idx % ncols]
        y = da.isel(variable=0).values
        t = da.coords["time"].values
        ax.plot(t, y, color=colors[idx], linewidth=0.6)
        ax.set_title(name, fontsize=7, pad=2)
        ax.tick_params(labelsize=5)
        ax.set_xlabel("")
        ax.set_ylabel("")

    # hide unused axes
    for idx in range(n, nrows * ncols):
        axes[idx // ncols, idx % ncols].set_visible(False)

    fig.tight_layout()
    plt.show()

(a) TVBO Dynamics Database — models by category

Summary

Code

import pandas as pd
rows = []
for cat, title in CATEGORIES:
    attempted = len(Dynamics.list_db(model_type=cat))
    succeeded = len(results[cat])
    rows.append({"Category": title, "Available": attempted, "Plotted": succeeded})
pd.DataFrame(rows)

Table 1

	Category	Available	Plotted
0	Mean-Field Models	11	11
1	Neural-Mass Models	19	19
2	Phase-Oscillator Models	3	3
3	Phenomenological Models	3	3
4	Spiking / Single-Neuron Models	17	16
5	Generic Dynamical Systems	53	53