reshaping-neural-representation-presynaptic-plasticity
CommunityAssociative presynaptic STP for neural modeling
Education & Research#fisher-information#neural-dynamics#tsodyks-markram#associative-stp#presynaptic-plasticity#information-theoretic-learning#temporal-coding
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Associative presynaptic short-term plasticity (STP) modeling that jointly considers pre- and postsynaptic activity and optimizes information transmission under resource constraints, enabling more realistic neural dynamics and improved temporal processing.
Core Features & Use Cases
- Associative STP: depends on pre- and postsynaptic coactivation for flexible dynamics.
- Information-theoretic learning: maximizes stimulus information subject to resource constraints.
- Temporal-coding capabilities: phase-sensitive onset detection and rapid reconfiguration in recurrent circuits.
Quick Start
Configure a Tsodyks-Markram STP model and run the Fisher information-based learning rule to maximize information under resource constraints.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: reshaping-neural-representation-presynaptic-plasticity Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#reshaping-neural-representation-presynaptic-plasticity Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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