Welcome to the scripts used in Modeling the Face recognition system in the brain
This paper explores the intersection of artificial intelligence and neuroscience, focusing on face recognition dynamics. Inspired by artificial neural networks surpassing human performance, the study investigates whether Convolutional Neural Networks (CNNs) trained for face recognition mimic the neural dynamics of face recognition in the human brain. Utilizing Magnetoencephalography (MEG) and comparing activations across seven CNNs, the research leverages high temporal resolution and source reconstruction techniques to unveil spatio-temporal similarity patterns. The study contributes novel insights into the complex interplay between artificial and biological neural responses associated with face recognition.
Here's an overview of the key folders and their contents:
This folder showcases a collection of scripts meticulously crafted to prepare data for a variety of experiments within the project. The scripts are organized into distinct categories, including HDF5 and repartition scripts, aligning with the unique structures of our datasets. The goal is to ensure efficient and tailored data processing for the diverse experiments ahead.
Containing scripts for processing Magnetoencephalography (MEG) data, this repository focuses on a multi-subject, multi-modal human neuroimaging dataset available on OpenNeuro. The dataset features simultaneous MEG/EEG recordings during a visual recognition task, providing valuable insights into neural responses. From data acquisition details to processing steps, this repository covers the intricate journey of handling complex neuroimaging data.
Dive into the world of model architecture, training, and feature extraction in the Models subfolder. This collection includes scripts showcasing a variety of models, each selected for its unique approach and method. Emphasizing diverse building blocks, this subfolder offers a rich exploration of neural network models for your perusal.
The Similarity Analysis folder is at the forefront of advanced neuroimaging analyses. Explore scripts dedicated to Representational Dissimilarity Matrices (RDMs), Representational Similarity Analyses (RSAs), and noise ceiling estimation. These analyses provide a nuanced understanding of neural representations, offering valuable insights into the brain's information organization.
The Utils folder houses utility modules and resources indispensable for various aspects of the study. From handling image datasets to providing essential support functions, these utilities play a crucial role in the project's success.
This project received funding from the UNIQUE (Unifying AI and Neuroscience) research center, Cerebrum, and CIRCA.