Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-braid-input-driven-neural-behavioral-dynamicsgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-braid-input-driven-neural-behavioral-dynamics/SKILL.md--- name: braid-input-driven-neural-behavioral-dynamics version: 1.0 date: 2026-04-22 paper: "2509.18627" title: "BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data" description: "BRAID deep learning framework that models nonlinear neural dynamics underlying behavior while explicitly incorporating external inputs. Disentangles intrinsic recurrent dynamics from input effects using forecasting objectives." category: neural-dynamics tags: [neural-dynamics, behavioral-modeling, recurrent-neural-network, input-driven, motor-cortex, disentanglement] --- # BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data ## Summary Deep learning framework for modeling nonlinear neural dynamics underlying behavior while explicitly incorporating measured external inputs (sensory stimuli, upstream regions, neurostimulation). Disentangles intrinsic recurrent dynamics from input effects. ## Core Methodology ### Problem - Neural populations are often modeled as autonomous dynamical systems - External inputs (sensory stimuli, neurostimulation) shape population activity but are ignored - Need to disentangle intrinsic dynamics from input-driven effects ### BRAID Framework 1. **Input-Driven RNN**: Incorporates measured external inputs into recurrent neural network model 2. **Forecasting Objective**: Includes prediction task to learn dynamics that generalize 3. **Multi-Stage Optimization**: Prioritizes learning of intrinsic dynamics related to behavior of interest 4. **Disentanglement**: Separates intrinsic recurrent dynamics from external input effects ### Key Components - Input-driven recurrent neural network architecture - Neural-behavioral multi-modal learning - Behavioral relevance constraint on learned dynamics - Forecasting-based intrinsic dynamics extraction ### Validation - **Nonlinear simulations**: Accurately recovers shared intrinsic dynamics between neural and behavioral modalities - **Motor cortex data**: More accurately fits neural-behavioral data by incorporating sensory stimuli - **Forecasting**: Improves prediction of neural-behavioral data vs baseline methods ## Applications - Motor cortical activity modeling during tasks - Neural dynamics with sensory input decomposition - Neurostimulation effect analysis - Brain-behavior relationship modeling ## Activation Triggers neural dynamics, behavioral modeling, input-driven, RNN, motor cortex, disentanglement, neural population, recurrent dynamics ## Activation Keywords - braid-input-driven-neural-behavioral-dynamics - braid input driven - braid input driven neural behavioral dynamics ## Tools Used - `read` - 读取技能文档 - `write` - 创建输出 - `exec` - 执行相关命令 ## Instructions for Agents 1. 理解技能的核心方法论 2. 根据用户问题提供针对性回答 3. 遵循最佳实践 ## Examples ### Example 1: 基本查询 **User:** 请解释 Braid Input Driven Neural Behavioral Dynamics **Agent:** Braid Input Driven Neural Behavioral Dynamics 是关于...