I took an online free Coursera class concerning Computational Neuroscience. My sister was a neuroscience major and inspired me to learn a little bit more about it.

[WIP] Prototype desktop application for working with NeuroML models

papers referred in Computational Neuroscience course offered by the University of Washington

predictive machine learning classification and data processing on neuroscience brain image data

Simulations of neural models and learning algorithms.

Code associated with the article "[Rp] Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model"

Repository holding material and assignments for the Computational Neuroscience course

Source code for the website

Simulates image processing in the visual cortex using Gabor and DoG filters in spiking neural networks.

An implementation of the Neuron By Neuron training algorithm in Python. The only true successful DEEP learning algorithm available today.

Use Chaotic time intervals as input to Hodgkin-Huxley neuron model in order to simulate real-world random stimuli.

Reverse Engineering the Gate Composition given the Voltage Trace of a Gate

Modeling ideas related to neuroscience.

Comprehensive pre-processing and analysis of EEG/MEG data using advanced machine learning and statistical techniques. Includes data cleaning, feature extraction, and visualization to extract meaningful insights from neuroimaging data.

Assignments of Bilkent's Computational Neuroscience course. Includes a lot about Machine Learning and Statistics. (2018-2019)

BNDF On-Premise local cluster

Data for the Quantitative Single-Neuron Modeling Competition (2007).

A study of the information theory and machine learning paradigms applied in the brain.

My personal branch of NMA materials.