skills.rest
hiyenwong
hiyenwongCOMMUNITY
Explore all skills by this author →
𝕏@khonsu_51GitHub

neuromodulated-synaptic-plasticity

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

2|Updated Feb 12, 2026
Search Tags:#neuroscience#meta-learning#online-learning#spiking-neural-network#neuromodulated#synaptic-plasticity#learning-to-learn
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill neuromodulated-synaptic-plasticity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neuromodulated-synaptic-plasticity
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/neuromodulated-synaptic-plasticity
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill neuromodulated-synaptic-plasticity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Neuromodulated synaptic plasticity frameworks enable online, neuroscience-inspired learning in spiking neural networks, addressing the difficulty of adapting models in real time.

Core Features & Use Cases

  • Online adaptation of SNNs using meta-learned plasticity rules guided by neuromodulator signals.
  • Supports meta-learning workflows and continual learning in neuroscience-inspired AI systems.
  • Use Case: researchers test rapid adaptation to changing stimuli in neuromorphic experiments.

Quick Start

Initialize a neuromodulated plasticity model and run a single online-adaptation trial on a benchmark task.

Frequently Asked Questions about neuromodulated-synaptic-plasticity

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

FAQPage Schema
How do I implement online learning in spiking neural networks?▼

Apply meta-learned gradient-descent plasticity rules parameterized by neuromodulator signals to enable real-time online weight updates within a modular spiking neural network architecture.

What is neuromodulated synaptic plasticity in SNN models?▼

Neuromodulated synaptic plasticity is a neuroscience-inspired mechanism enabling spiking neural networks to adapt continually, using neuromodulator signals to guide meta-learned rules for rapid stimulus adaptation.

Can I use meta-learning for few-shot learning in neuromorphic experiments?▼

Yes, you can use meta-learning for few-shot learning in neuromorphic experiments by parameterizing gradient-descent learning rules with meta-learned variables and neuromodulator signals to drive rapid adaptation.

How do I run continual adaptation tasks with neuromodulated SNNs?▼

Run continual adaptation tasks with neuromodulated SNNs by initializing a modular spiking architecture and executing online-adaptation trials driven by meta-learned plasticity rules and neuromodulator signals.

Does this online learning approach require a specific SNN architecture?▼

Yes, neuromodulated online learning requires a modular spiking neural network architecture that supports gradient-descent-based learning rules parameterized by meta-learned variables and neuromodulator signals.

Related Skills

Explore More Education & Research Skills→
hiyenwonghiyenwong
2

sparse-gradient-plasticity

Implement sparse gradient-based synaptic plasticity for online learning in spiking neural networks.

Community
Advanced
rongarederongarede
4

makepad-evolution

Automate Makepad skill improvement through self-evolution, correction, and validation.

Community
Advanced
hiyenwonghiyenwong
2

decolle-snn-learning

Train spiking neural networks online with synthetic gradients in PyTorch.

Community
Advanced
hiyenwonghiyenwong
2

multi-plasticity-synergy-snn

Coordinate multiple plasticity mechanisms to train spiking neural networks.

Community
Advanced