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stochastic-synaptic-plasticity

Simulate STDP-based stochastic synaptic plasticity with pair-based and triplet rules.

2|Updated Feb 12, 2026
Search Tags:#neuroscience#stdp#synaptic-plasticity#stochastic-modeling#weight-evolution#markov-process#triplet-rule
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill stochastic-synaptic-plasticity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stochastic-synaptic-plasticity
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/stochastic-synaptic-plasticity
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill stochastic-synaptic-plasticity

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about stochastic-synaptic-plasticity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I simulate synaptic weight evolution under spike-timing dependent plasticity rules?▼

You can simulate pair-based and triplet STDP rules by configuring the STDP parameters and invoking the simulation pipeline. This tracks spike histories and analyzes synaptic weight behavior across varying pre- and post-synaptic spike rates and correlations.

What is stochastic synaptic plasticity and how does a Markov process model it?▼

Stochastic synaptic plasticity formalizes synaptic evolution as a Markov-style process to study learning dynamics in neural networks. This approach enables steady-state analyses and rate-dependent behavior studies by modeling weight updates probabilistically rather than deterministically.

Can I use pair-based and triplet STDP rules in the same synaptic plasticity simulation?▼

Yes, the model supports both pair-based and triplet STDP rules to capture simple and higher-order spike interactions. This allows you to perform comparative rule analysis and observe how each rule affects long-term synaptic strength under varying spike rates and correlations.

Does this stochastic STDP model require external dependencies to run?▼

No, the stochastic STDP model does not require external dependencies. It includes a self-contained Python implementation that provides a PlasticityKernel and configurable STDP parameters to demonstrate weight updates, spike-history tracking, and weight distribution analyses.

What are the limitations of using pair-based STDP rules for synaptic weight updates?▼

Pair-based STDP rules capture simple spike interactions but may not fully represent higher-order temporal correlations. The model addresses this by supporting triplet rules, which better capture complex spike interactions and their effects on long-term synaptic strength and weight clipping behavior.

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