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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users.
* More info + downloads: https://mlpack.org
* Git repo: https://github.com/mlpack/mlpack
This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet.
The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people.
Multiscale Neuroscience and Systems Biology Simulator
Moose is the core of a modern software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes.
We have moved Github.com. This should be your source for the latest version of the code.
NEVESIM is an event-driven neural simulation tool.
NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events...
EZO AssetSonar is a comprehensive IT asset management platform that provides real-time visibility into your entire digital infrastructure. Track and optimize hardware, software, and license management to reduce risks, control IT spend, and improve compliance.
PCSIM is a tool for distributed simulation of heterogeneous networks composed of different model neurons and synapses. The development of PCSIM was supported by the FACETS EU project.
The Genetic Architecture Framework is intended to explore and experiment with artificial life techniques using a genetic base for the physical and neural networks for the brain, in a game based simulated world.