OPEN-SOURCE POPULATION DYNAMICS LAB
Neural Population Dynamics Explorer
Change a circuit parameter and follow its effect from the firing-rate trace to the phase portrait and the eigenvalues.
SIMULATION
Follow the trajectory
Firing rates
Population activity over time
Phase portrait
Click the plot to try a new initial state
LOCAL ANALYSIS
Equilibria & stability
| Rates (Hz) | Eigenvalues (s⁻¹) | Local behavior |
|---|
FIELD NOTES
From equations to neural data
Try a one-parameter experiment
Take it into Python
Export the simulated traces as CSV and load them with pandas.read_csv. For recorded spikes, divide each population’s binned spike counts by the bin width in seconds and the number of neurons. Plot the result against time and compare relaxation times or the spacing between peaks.
The solver uses fourth-order Runge–Kutta with steps of at most 0.5 ms. The CSV contains the samples displayed in the plots. These illustrative models are for learning and exploration; they are not fitted to a person’s neural recordings.