multi-plasticity-synergy-snn

Coordinate multiple plasticity mechanisms to train spiking neural networks.

2|Updated Feb 12, 2026

Search Tags:#plasticity #snn #spiking-neural-network #stdp #neuromodulation #adaptive-mechanisms

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill multi-plasticity-synergy-snn

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Biologically inspired SNN training framework that enables collaboration among multiple plasticity mechanisms while preserving relatively independent update dynamics.

Core Features & Use Cases

Integrates STDP, reward-modulated plasticity, homeostatic plasticity, and neuromodulated plasticity for flexible learning.
Adaptive mechanism gates dynamically balance updates to improve robustness across tasks.
Use case: train spiking neural networks on static image datasets and event-based data, bridging neuromorphic and conventional AI workflows.

Quick Start

Initialize a Multi-Plasticity Synergy SNN model and train with STDP, reward-modulated, and homeostatic updates using adaptive mechanism gates.

Frequently Asked Questions about multi-plasticity-synergy-snn

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

FAQPage Schema

How do I combine STDP and reward-modulated plasticity in spiking neural network training?▼

You can combine STDP and reward-modulated plasticity in spiking neural network training by using an adaptive mechanism gate to coordinate multiple plasticity mechanisms, dynamically balancing updates to preserve relatively independent dynamics.

What is neuromodulated plasticity in SNNs and when do I need it?▼

Neuromodulated plasticity dynamically adjusts SNN learning rules based on global signals. You need it for neuromorphic learning tasks requiring hybrid learning rules to enhance robustness across static and dynamic datasets.

Can I use multiple plasticity mechanisms for event-based datasets?▼

Yes, coordinating multiple plasticity mechanisms applies to event-based data and static image datasets. The adaptive mechanism gates dynamically balance updates to bridge neuromorphic and conventional AI workflows.

How do I start training a spiking neural network with hybrid learning rules?▼

Initialize a Multi-Plasticity Synergy SNN model and train it with STDP, reward-modulated, and homeostatic updates. The adaptive mechanism gates will automatically coordinate the hybrid learning rules during the process.

Why does my spiking neural network training need homeostatic plasticity alongside STDP?▼

Homeostatic plasticity prevents runaway firing rates during STDP training. Combining them through an adaptive gate ensures multiple plasticity mechanisms collaborate effectively while preserving relatively independent update dynamics for robust learning.

What are the limitations of combining multiple plasticity mechanisms in SNNs?▼

While combining multiple plasticity mechanisms enhances robustness, the primary constraint is balancing independent update dynamics. The adaptive mechanism assignment gate mitigates conflicts but requires careful tuning for optimal neuromorphic learning task performance.

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Jointly learn synaptic weights and intrinsic parameters in spiking neural networks with PyTorch.

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neuromodulated-synaptic-plasticity

Implements gradient-descent-based learning rules with meta-learned variables and neuromodulator signals for online adaptation of spiking neural networks.

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