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neural-dynamics-decision-making

Fit parameterized accumulator models to spike-train data with variational Laplace EM.

2|Updated Feb 12, 2026
Search Tags:#decision-making#neural#drift-diffusion#ddm#neural-decoding#accumulator#rnn-ssm
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill neural-dynamics-decision-making
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neural-dynamics-decision-making
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/neural-dynamics-decision-making
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill neural-dynamics-decision-making

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unified framework to model neural decision dynamics by connecting decision theory with neural activity, enabling integrated analysis and interpretation of neural decision processes.

Core Features & Use Cases

  • Support for classic drift-diffusion (DDM), multi-dimensional accumulators, variable/collapsing boundaries, and discrete jumps in decision dynamics.
  • Parameterized RNN-based state-space models (RNN-SSM) with variational Laplace EM inference for fitting spike data and inferring latent decision states.
  • Applications include neural decoding, model comparison across decision architectures, and classification of accumulator vs sensory neurons.

Quick Start

Analyze spike-train data by fitting a multi-dimensional accumulator model with collapsing boundaries and infer hidden decision states using variational Laplace EM.

Frequently Asked Questions about neural-dynamics-decision-making

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

FAQPage Schema
How do I fit a drift-diffusion model to spike-train data for neural decoding?▼

This framework fits multi-dimensional accumulator models with collapsing boundaries to spike-train data and infers hidden decision states using variational Laplace EM.

What is the best way to model dynamic decision boundaries in cortical circuits?▼

Model variable and collapsing boundaries in cortical circuits by parameterizing them within a unified neural decision dynamics framework connecting decision theory with neural activity.

How does variational Laplace EM work for RNN state-space model inference?▼

Variational Laplace EM infers latent decision states for RNN-based state-space models by fitting parameterized architectures directly to spike data, enabling integrated neural decision analysis.

Can I compare different accumulator models to classify sensory versus accumulator neurons?▼

Yes, you can perform model comparison across accumulator architectures to classify accumulator versus sensory neurons and evaluate dynamic boundaries in cortical circuits.

Does this neural decision dynamics framework support discrete jumps in decision modeling?▼

Yes, the framework supports discrete jumps alongside classic drift-diffusion, multi-dimensional accumulators, and variable or collapsing boundaries for comprehensive neural data analysis.

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