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

gtas-generative-spike-train-model

Generate correlated neural spike trains with tunable thinning probabilities and shifts using GTaS model.

2|Updated Feb 12, 2026
Search Tags:#correlation#neural#shift#thinning#gtas#spike-train#cumulant
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill gtas-generative-spike-train-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gtas-generative-spike-train-model
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/gtas-generative-spike-train-model
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill gtas-generative-spike-train-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GTaS Generative Spike Train Model provides a principled framework to generate correlated neural spike trains by starting from a marginal Poisson process and applying thinning and time-shift operations to induce controllable higher-order temporal dependencies, enabling analytical characterization of spike train statistics.

Core Features & Use Cases

  • Generate multi-neuron spike trains with tunable thinning probabilities and shifts to create 2nd- and higher-order correlations.
  • Analyze cumulants and higher-order statistics to study how temporal structure affects network dynamics and decoding.
  • Use as synthetic data for testing neural decoding algorithms, network simulations, and methodological validation.

Quick Start

Define a Poisson baseline rate, apply thinning with probability p, apply a shift Δ, generate the trains, and compute cumulants for subsequent analysis.

Frequently Asked Questions about gtas-generative-spike-train-model

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

FAQPage Schema
How do I generate correlated neural spike trains from a Poisson baseline?▼

Generate correlated spike trains by defining a marginal Poisson baseline rate, applying parameterizable thinning probabilities, introducing time shifts, and computing cumulants for subsequent higher-order temporal dependency analysis.

How does thinning and shifting create higher-order correlations in spike trains?▼

Thinning and shifting induce higher-order correlations by selectively dropping spikes from a marginal Poisson process and applying temporal offsets, creating structured dependencies for analytical characterization of spike train statistics.

Can I use synthetic spike trains to test neural decoding algorithms?▼

Yes, you can use generated synthetic spike trains with tunable thinning probabilities and shifts to test neural decoding algorithms, simulating ensembles for methodological validation and network simulations.

What is the best way to assess higher-order cumulants in neural data analysis?▼

Assess higher-order cumulants by generating correlated spike trains with parameterizable thinning probabilities and time shifts to create controlled temporal structure, enabling analytical characterization of network dynamics and decoding.

What are the limitations of using thinning and shift operations for spike train generation?▼

Limitations include dependency on a Poisson baseline process and restriction to controllable temporal dependencies induced solely by parameterizable thinning probabilities and time shifts, which may constrain complex network dynamics representation.

Related Skills

Explore More Data & Analytics Skills→
hiyenwonghiyenwong
2

spike-image-decoder

Reconstruct visual scenes from neural spike data using a deep learning decoder.

Community
Advanced
hiyenwonghiyenwong
2

neural-dynamics-decision-making

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

Community
Advanced
NeuroAIHubNeuroAIHub
269

neural-decoding-analysis

Guide multivariate neural decoding and RSA method selection for fMRI, EEG, and MEG research.

Official
Advanced
ntluong95ntluong95

Time Series in R

Manage time series structures and autocorrelation diagnostics in R.

Community
Intermediate