associative-presynaptic-plasticity

Learn Tsodyks-Markram synapse parameters with Fisher information rules.

2|Updated Feb 12, 2026

Search Tags:#associative #presynaptic #short-term plasticity #fisher information #tsodyks-markram #neural modeling

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill associative-presynaptic-plasticity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: associative-presynaptic-plasticity
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/associative-presynaptic-plasticity
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill associative-presynaptic-plasticity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This method provides a principled framework to learn associative presynaptic short-term plasticity by extending Fisher-information-based rules to Tsodyks-Markram synapses, deriving baseline weights and release probability updates.

Core Features & Use Cases

Model Tsodyks-Markram synapse dynamics with parameters U, D, F, and A, including state updates and spike processing.
Apply Fisher-information learning rules to optimize synaptic weights and release probability for improved information transmission.
Use in neural modeling of temporal coding, sequence learning, and online adaptation in neuromorphic systems.

Quick Start

Train the associative STP learner on sequential spike data to adjust synaptic weights and release probabilities.

Frequently Asked Questions about associative-presynaptic-plasticity

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

FAQPage Schema

How do I optimize Tsodyks-Markram synapse parameters for temporal coding?▼

Fisher information guides presynaptic short-term plasticity by deriving gradient-based updates for synaptic weights and release probability U, maximizing information transfer during spike processing in Tsodyks-Markram neural modeling.

How do I train a short-term plasticity model on sequential spike data?▼

To train associative STP on sequential spike data, process Tsodyks-Markram state updates and apply gradient-based updates to adjust synaptic weights and release probability U for online adaptation in neural modeling.

Can I use Fisher information to update release probability U in neural modeling?▼

Yes, you can use Fisher information learning rules to update release probability U in neural modeling by deriving gradient-based updates with adjustable constraints, optimizing information transmission across Tsodyks-Markram synapses.

Does associative presynaptic plasticity work for sequence learning in neuromorphic systems?▼

Yes, associative presynaptic plasticity supports sequence learning in neuromorphic systems by modeling Tsodyks-Markram dynamics to enable online adaptation and optimize temporal coding across simulated spike sequences.

What are the constraints when adjusting release probability U in Tsodyks-Markram synapses?▼

When adjusting release probability U in Tsodyks-Markram synapses, constraints apply to gradient-based weight updates to ensure principled short-term plasticity dynamics and maintain stable information transmission during spike processing.