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The Role of E/I modulation in Shaping Neural Dynamics in Decision-Making and Adaptive Learning

Overview

This repository contains an analysis of the role of excitation and inhibition in shaping neural dynamics in two settings:

  • Decision-Making* --- using a mean-field Wilson-Cowan rate model
  • Adaptive Learning in Dynamic Environments (Continual Reinforcement Learning in a changing sequence of Probabilistic Reversal Learning (PRL) tasks) --- using a Neural Actor Critic model with an E/I modulation mechanism

Directory Structure

1. mean_field__decision_making/

This folder contains notebooks and scripts for simulating and analyzing neural dynamics in decision-making processes using mean-field approximations.

Files:

  • evidence_accum_perf_crit.ipynb: For understanding evidence accumulation in achieving performance criteria during decision-making tasks.
  • inhib.ipynb: Investigates the impact of inhibition on neural dynamics.
  • plotting.py: Helper functions for visualizing results and simulations.
  • recur_excit.ipynb: Studies the role of recurrent excitation in decision-making dynamics.
  • role_inhib.ipynb: Focuses on the role of inhibition in decision-making processes.
  • sim_dyn.py: Core simulation script for dynamic systems modeling.
  • utils.py: Utility functions for preprocessing, computation, and support tasks.

2. neuralAC__adaptive_learning/

This folder contains code for implementing and analyzing a Neural Actor-Critic (NeuralAC) framework for adaptive learning.

Files:

  • baselines.ipynb: Compares the performance of over wE.
  • environment.py: Defines the environment setup for reinforcement learning tasks.
  • experiment.py: Contains code for running experiments and simulations.
  • neural_actor_critic.py: Implements the Neural Actor-Critic algorithm.
  • plotting.py: Visualization scripts specific to adaptive learning experiments.
  • role_inhib_strong_recur.ipynb: Examines the role of inhibition in recurrent networks with high precision.
  • role_inhib_weak_recur.ipynb: Examines the role of inhibition in recurrent networks with low precision.
  • utils.py: Common utility functions used across experiments.

Getting Started

Prerequisites

  • Python 3.8 or higher
  • Recommended packages:
    • numpy
    • matplotlib
    • scipy
    • jupyter
    • pandas

Installation

  1. Clone the repository: 
    git clone <repository_url>
cd EL_dynamics
  2. Install dependencies: 
    pip install -r requirements.txt

Running the Code

  1. Navigate to the desired folder: 
    cd mean_field__decision_making
# or
cd neuralAC__adaptive_learning
  2. Open the notebooks in Jupyter: 
    jupyter notebook <notebook_name>.ipynb

Key Features

  • Simulations of mean-field neural dynamics for decision-making.
  • Analysis of recurrent excitation and inhibition mechanisms.
  • Neural Actor-Critic implementation for adaptive learning.
  • Visualization tools for understanding results.