spike-timing-neuronal-assemblies

STDP drives neuronal assembly formation by strengthening synapses when pre-synaptic spikes precede post-synaptic ones, creating shared stimulus preferences within neural clusters that are maintained during spontaneous activity.

Yes, you can simulate noise correlations and evaluate their encoding influence. The framework analyzes how these correlations affect information processing across trained neuronal assemblies.

You train neuronal assemblies by running the Python simulation routines with configurable parameters, such as specifying 100 neurons and 5 assemblies over 20 training epochs.

The best way to analyze weight evolution is running the end-to-end pipeline to train networks on stimulus sets, which evaluates how STDP shapes within-assembly versus between-assembly connections over time.

No external dependencies are required. The framework implements a self-contained Python environment with all necessary STDP modeling, spontaneous activity, and analysis routines included.

Spike timing shapes strong stimulus preferences because STDP reinforces connections between neurons that fire together with precise timing, leading to specialized assembly responses during ongoing spontaneous dynamics.