42 Open Source Neurons Software Projects
Free and open source neurons code projects including engines, APIs, generators, and tools.
Neataptic 1060 ⭐
:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js
Synthesizing 475 ⭐
Code for paper "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
Spiking Neural Network Snn With Pytorch Where Backpropagation Engenders Stdp 173 ⭐
What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.
Simple Neural Network 140 ⭐
Creating a simple neural network in Python with one input layer (3 inputs) and one output neuron.
Micromlp 119 ⭐
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
Spiketorch 82 ⭐
Experiments with spiking neural networks (SNNs) in PyTorch. See https://github.com/BINDS-LAB-UMASS/bindsnet for the successor to this project.
Neuromorphovis 84 ⭐
A lightweight, interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks.
Corbinian 14 ⭐
CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations
Nat.nblast 13 ⭐
R package implementing the NBLAST neuron search algorithm, as an add-on for the NeuroAnatomy Toolbox (nat) R package.
Nest Stdpmodule 12 ⭐
A generalizable model of spike-timing dependent plasticity for the Neural Simulation Tool (NEST).
Grid Cells 19 ⭐
Implementation of Vector Based Navigation using Grid-like cells using Tensorflow and Numpy
Aika 51 ⭐
Aika is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses representing the knowledge the network has already acquired and another graph consisting of activations and links describing the information the network was able to infer about a concrete input data set. There is a one-to-many relation between the neurons and the activations. For example, there might be a neuron representing a word or a specific meaning of a word, but there might be several activations of this neuron, each representing an occurrence of this word within the input data set. A consequence of this decision is that we have to give up on the idea of a fixed layered topology for the network, since the sequence in which the activations are fired depends on the input data set. Within the activation network, each activation is grounded within the input data set, even if there are several activations in between. This means links between activations serve two purposes. On the one hand, they are used to sum up the synapse weights and, on the other hand they propagate the identity to higher level activations.
Brabenetz 18 ⭐
🧠 A fast and clean supervised neural network in C++, capable of effectively using multiple cores
N2a 14 ⭐
An object-oriented language for modeling large-scale neural systems, along with an IDE for writing and simulating models.
Snn 12 ⭐
This is a repository with implementations of neuron models, synapses, and spiking neural networks (SNN). It's still in development and it has original content in terms of code.
Realneuralnetworks.jl 10 ⭐
A unified framework for skeletonization, morphological analysis, and connectivity analysis.
Neugen 10 ⭐
NeuGen is made for generation of dendritic and axonal morphology of realistic neurons and networks in 3D
Micronn 10 ⭐
Micro neural network with multi-dimensional layers, multi-shaped data, fully or locally meshing, conv2D, unconv2D, Qlearning, ... for test!
Neurox 12 ⭐
A Python library that encapsulates various methods for neuron interpretation and analysis in Deep NLP models.