Code for the assignments for the Computational Neuroscience Course BT6270 in the Fall 2018 semester

NeurIPS AMHN 2023: Neural recordings artifacts preprocessing model and pipeline. Hopfield Deep Neural Networks for Artifact preprocessing in Brain State Decoding

Lab experiments of Soft Computing Techniques

A Hopfield network to reconstruct patterns (numerical digits) and cope with noise.

Hebbian Learning Rule

Implementation of a Hopfield network in Python

Projek C++ Neural Network

Hopfield network with implemented hebbian ad oja learning rules.

In this tutorial, we explore the mathematical underpinnings of Hebbian learning within Hopfield networks, emphasizing its role in pattern recognition.

Identifying the origin of fish from the growth-ring diameter of scales using Neural Networks

Hopfield Associative Memory with the Hebb rule (without any NN library) for Neural Network course at Warsaw University of Technology

This repository contains the python implementations of a few soft computing algorithms.

Octave implementation of some basic neural networks

This repository is dedicated to the lab work completed for the CCAI 321 course. It demonstrates practical work in artificial neural networks, including the implementation of activation functions, Hamming networks, perceptron and Hebb learning rules, and two-layer networks in Python. Networks were trained and tested on both examples and real data.