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tsodyks-markram-chaotic-dynamics

Simulate chaotic Tsodyks-Markram networks and analyze Shilnikov bifurcations.

2|Updated Feb 12, 2026
Search Tags:#neuroscience#tsodyks-markram#chaotic-dynamics#shilnikov-bifurcation#bifurcation-analysis#lyapunov-exponent#deterministic-models
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill tsodyks-markram-chaotic-dynamics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tsodyks-markram-chaotic-dynamics
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/tsodyks-markram-chaotic-dynamics
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill tsodyks-markram-chaotic-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables researchers to analyze and simulate chaotic dynamics in deterministic Tsodyks-Markram networks, revealing how Shilnikov-like bifurcations drive unpredictable activity.

Core Features & Use Cases

  • Deterministic TM network simulation with configurable synaptic dynamics (U, tau_rec, tau_facil) and neuron parameters.
  • Bifurcation and chaos analysis tools including Lyapunov exponent estimation and homoclinic orbit detection.
  • Use cases in computational neuroscience education, synaptic plasticity modeling, and nonlinear dynamics research.

Quick Start

Configure the TMModelConfig with U, tau_rec, and tau_facil, then create a TMNetworkModel and run simulate with a simple input function.

Frequently Asked Questions about tsodyks-markram-chaotic-dynamics

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

FAQPage Schema
How do I simulate chaotic dynamics in a Tsodyks-Markram neural network?▼

To simulate chaotic dynamics in a Tsodyks-Markram network, configure TMModelConfig with U, tau_rec, and tau_facil parameters, then run the simulation using TMNetworkModel with a defined input function to analyze deterministic unpredictable neural activity.

What is a Shilnikov bifurcation in deterministic synaptic plasticity models?▼

A Shilnikov bifurcation in deterministic synaptic models is a mathematical phenomenon driving chaotic dynamics via homoclinic orbits. The ShilnikovBifurcationAnalyzer detects these orbits to reveal how unpredictable activity arises in Tsodyks-Markram networks.

Can I estimate Lyapunov exponents for Tsodyks-Markram synapses using Python?▼

Yes, you can estimate Lyapunov exponents for Tsodyks-Markram synapses using Python. The Skill provides built-in chaos analysis tools that calculate Lyapunov exponents using numpy and scipy to quantify sensitivity to initial conditions in deterministic network dynamics.

Do I need scipy and matplotlib to perform bifurcation analysis on TM networks?▼

Yes, you need scipy and matplotlib along with numpy to perform bifurcation analysis on TM networks. These Python dependencies are required to run deterministic Tsodyks-Markram simulations, compute bifurcation metrics, and visualize the resulting chaotic neural dynamics.

Why does my deterministic Tsodyks-Markram model show unpredictable activity?▼

Your deterministic Tsodyks-Markram model shows unpredictable activity because specific synaptic parameter configurations trigger Shilnikov-like bifurcations. These bifurcations cause deterministic chaos, making the system highly sensitive to initial conditions despite lacking stochastic noise.

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