stochastic-synaptic-plasticity

What problem does it solve?

The STDP-based stochastic synaptic plasticity model provides a rigorous computational framework to simulate how synaptic weights evolve under spike-timing dependent plasticity, enabling researchers to study learning-like dynamics in neural networks.

Core Features & Use Cases

• Supports pair-based and triplet STDP rules to capture both simple and higher-order spike interactions.
• Formalizes synaptic evolution via a stochastic, Markov-style approach, enabling steady-state analyses and rate-dependent behavior.
• Includes a Python implementation that demonstrates weight updates, spike-history tracking, and comparative rule analysis for educational and research purposes.
• Use Case: Investigate how varying pre/post-synaptic spike rates and correlations shape long-term synaptic strength.

Quick Start

Run the STDP simulation pipeline by creating a PairBasedKernel with the provided STDPConfig and invoking StochasticSynapticPlasticity.simulate to observe synaptic weight evolution.