Use DNNs to build encoding models of EEG visual responses.
This repository contains homework assignments and projects completed for the course "Advanced Topics in Neuroscience" instructed by Dr. Ali Ghazizadeh at Sharif University of Technology.
Movie-trained transformer reveals novel response properties to dynamic stimuli in mouse visual cortex
This repository is a collection of my learning and exploration of the latest research, concepts, and techniques in the field of neuroscience. I have organized the content into folders based on each assignment and the corresponding papers. Feel free to browse through the repository, read the papers, and examine my work.
Comprehensive Computational Neuroscience course materials (NYCU LSNS30045). Includes Python-based implementations of neural encoding models, spike-triggered averages, LIF & Hodgkin-Huxley simulations, with lecture notes, homework, and real neural data.
Scalable creation of audio datasets for generating stem continuations of music files
An R package for testing and analyzing rate fluctuations of multiplexing neurons under a Bayesian paradigm
Material from the Neuroengineering course
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