https://github.com/francesco-mannella/active-inference-whiskers-model

https://github.com/francesco-mannella/active-inference-whiskers-model

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• Host: GitHub
• URL: https://github.com/francesco-mannella/active-inference-whiskers-model
• Owner: francesco-mannella
• License: cc0-1.0
• Created: 2023-04-26T14:31:43.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-09-28T13:28:20.000Z (over 1 year ago)
• Last Synced: 2024-11-07T09:23:58.692Z (3 months ago)
• Language: Python
• Size: 1.67 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
### Active Inference Whiskers Model 

## Introduction

This project inplements an Active Inference model that simulates a

rodent's behavior during object recognition. It is based on the idea that the

brain constantly engages in predictive processing and action selection to

minimize the discrepancy between its predictions and incoming sensory

information. The model includes a generative model that predicts the tactile

and visual consequences of interacting with objects through whisking.

Discrepancies between these predictions and actual sensory inputs result in

error signals, which are used to update the beliefs of the internal model and

optimize actions. The simulated rodent adapts its whisking behavior based on

the predicted dynamics of whiskers and can use visual information as a prior to

adapt whisker dynamics even before contact with the object. The model aims to

explain how whisker movements and visual information are combined during object

recognition.

 



 

## Installation

1. Create a virtual environment:

    *   Create a new directory for your project and navigate to it in the command line.

    *  Create the virual environment 

        

                    python -m venv myenv

    * Activate the virtual environment: 

        * for Windows:

    

                    myenv\Scripts\activate

        * for macOS/Linux:

    

                    myenv\bin\activate

2. Install packages from requirements.txt:

    * Navigate to the directory containing the requirements.txt file.

    *Run the command 

        

        pip install -r requirements.txt

## Description

    

The main modules    

    src/

    ├── analysis

    │   └── stats

    │       ├── crosscorr.py  -- methods for timeseries crosscorrelations 

    │       └── fourier_transform.py -- methods for FFT 

    └── simulation

        ├── ActiveInferenceModel.py -- python implementation of the Active Inference Model

        ├── run_simulation.py 

        │    └── run_simulation -- execute a single trial  

        └── generate_data.py 

             ├── run_trial -- execute a single trial  

             └── run_simulation -- execute a battery of n trials with all conditions 

                                   (obj0-touch)-(obj0-multimodal)-(obj1-touch)-(obj1-multimodal)  

## Usage

This is a simple example of how to use the simulator. For a more complete example see the test in **run_simulation.py**

        

```python

from src.simulation.run_simulation import run_simulation

# runs a single trial

data, frames = run_simulation()

# saves the simulation as a gif animation

make_simulation_gif_video(

    folder='.',

    root=f"video_filename",

    frames=frames,

)

```