synaptic-weight-distributions-plasticity-geometry
CommunityInfer plasticity geometry from synaptic weights
Data & Analytics#neuroscience#synaptic#weight-distribution#plasticity-geometry#mirror-descent#log-normal#non-euclidean
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Analyze synaptic weight distributions to infer the underlying geometry of plasticity, addressing mismatches between observed log-normal weights and Euclidean assumptions.
Core Features & Use Cases
- Analyze weight distributions to identify the underlying geometry (Euclidean, log, entropy) and infer plausible plasticity models.
- Simulate mirror-descent dynamics under different geometries and compare predicted weight distributions to empirical data.
- Validate geometry-driven hypotheses using cross-brain-region datasets and synthetic benchmarks.
Quick Start
Collect synaptic weight data and run the analysis to determine whether Euclidean, log, or entropy geometry best explains the observed distributions.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: synaptic-weight-distributions-plasticity-geometry Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#synaptic-weight-distributions-plasticity-geometry Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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