NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python
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Updated
May 31, 2024 - Python
NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python
unsupervised learning of natural images -- à la SparseNet.
Criticality analysis on complex networks using Power-law scaling functions, exponential relations, and phase transitions
In this project, I used Hebbian, Perceptron and Adaline neural networks to implement AND gate, and OR gate.
On the use of associative memory in Hopfield networks designed to solve propositional satisfiability problems
This repository has implementations of various alternatives to backpropagation for training neural networks.
Theory and practice of neural networks, deep learning and fuzzy logic. Excercises based on the course ECE447 of University of Thessaly.
In this tutorial, we explore the mathematical underpinnings of Hebbian learning within Hopfield networks, emphasizing its role in pattern recognition.
A lightweight and flexible framework for Hebbian learning in PyTorch.
Evolve Robotic Animats in a Simulation of Nature
Malleable spiking neural network framework and training platform.
Create Hebbian AI robots and evolve their brains to walk
Character recognition with Hamming & Hebb neural networks
Neuronal Networks Simulation
Pytorch Implementation, Experiments and Exploration of Biologically Plausible Neural Networks as a final project of CLPS1291 @ Brown
Demonstration of combining Resonate-and-Fire (RF) neurons and unsupervised hebbian learning to build a SNN capable of recognizing (complex) patterns in time data.
Quick guide to Hopfield Networks for image restoration and NP-hard problems.
A neural network model builder, leveraging a neuro-symbolic interface.
This repository encloses the programmatic part of the research into equivalence of Hebbian learning and the SVN formalism, exploring hypothesis brought forward in [On the equivalence of Hebbian learning and the SVM formalism [Nowotny, T and Huerta, R]
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