temporal-attention-graph-neural
CommunityDecode dynamic brain networks with attention
Data & Analytics#neuroscience#graph-neural-networks#neural-dynamics#temporal-attention#variational-graph#behavior-decoding#time-varying-connectivity
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Temporal dynamics of neuronal connections are challenging to capture. TAVRNN combines probabilistic graph learning with temporal attention to infer time-varying connectivity and relate it to behavior, delivering both single-unit dynamics and population-level explanations.
Core Features & Use Cases
- Time-varying graph modeling: Learn dynamic adjacency matrices across time steps.
- Explainable dynamics: Preserve neuron-level and group-level interpretability of connectivity changes.
- Predictive decoding: Decode behavior from learned representations; supports behavior prediction and clustering.
- Applications: Neural dynamics analysis, brain-machine interface development, and neuroscience discovery.
Quick Start
Train a TAVRNN on your neuronal activity sequence and inspect the learned time-varying graphs to interpret dynamic connectivity.
Dependency Matrix
Required Modules
None requiredComponents
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Please help me install this Skill: Name: temporal-attention-graph-neural Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#temporal-attention-graph-neural Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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