Popular GitHub Repositories for Neurons

simple neural network library in ANSI C

Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/

Simulation of spiking neural networks (SNNs) using PyTorch.

:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js

Code for paper "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

A python package for simulating movement and spatial cell types (e.g. place cells, grid cells) in continuous environments.

A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)

Creating a simple neural network in Python with one input layer (3 inputs) and one output neuron.

Using spiking neurons and spike-timing-dependent plasticity to classify the MNIST handwritten digits.

A library for finding knowledge neurons in pretrained transformer models.

A Transformer made of Rotation-equivariant Attention using Vector Neurons

Code for “Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation”

Neuron class provides LNU, QNU, RBF, MLP, MLP-ELM neurons

PyPi Package of Self-Organizing Recurrent Neural Networks (SORN) and Neuro-robotics using OpenAI Gym

The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.

[IJCAI-2022] Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?

Code for "Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation"

:traffic_light: An Artificial Neural Network based Traffic Light Controller for intersections. Computational Intelligence class final project.

Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

A Liquid State Machine using quantized neurons that are operating on lower-bit representations and fixed point computations. It provides a next step towards the implementation of efficient accelerators that can be used in the field of neuromorphic computing.