Simulators and tools

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Library — NeuroLab v0.0.9 documentation
ecet.ecs.ru.acad.bg/cst/docs/proceedings/s2/ii-27.pdf
Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Java Bidirectional Associative Memory Neural Network Java Bidirectional Associative Memory Neural Network
Emergent
Comparison of Neural Network Simulators - Emergent
Software NeuralEnsemble hosts a number of software projects for computational and systems neuroscience, including PyNN, NeuroTools, Brian, Neo, OpenElectrophy, libNeuroML and Sumatra. Some of the projects use our own Trac installation, others are on GitHub. Neural Ensemble :: Home Neural Ensemble :: Home
Requirements neuroConstruct has been tested on on WinXP/NT, Red Hat Linux and openSUSE and Mac OS (please let us know if you've any other experiences on other systems). A local installation of NEURON, GENESIS, MOOSE, PSICS or a PyNN compliant simulator will be needed to execute the simulation scripts generated by neuroConstruct. neuroConstruct neuroConstruct
Brian
PyNN (pronounced 'pine') is a is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON , NEST , PCSIM and Brian ). The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN - Trac PyNN - Trac
PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym".

PyBrain

PyBrain
Neural Modeling with Python (Part 3) So far we've looked at how to simulate a simple LIF model neuron and a complex Hodgkin-Huxley model neuron. The LIF neuron is computationally simple but physiologically implausible, while Hodgkin-Huxley gives us a very good representation of actual neural dynamics but is parameter-heavy and computationally expensive. An intriguing compromise between the two exists -- one that can generate a wide variety of observed neural spiking behavior while doing so with limited computational demand. It is called the quadratic integrate-and-fire model neuron, or simply Izhikevich neuron. Izhikevich Model Neuron Neural Modeling with Python (Part 3)
Brian/StimulusArrayGroup - NeuralEnsemble Cookbook - Trac This code is for creating a group of neurons which fire with a given 2D stimulus at a given rate. You initialise it with arguments: stimulus, which should be a 2D array with values between 0 and 1. rate, which should be a firing rate in Hz. onset, the time that the stimulus should become active. duration, the length of time for which the stimulus should be active. The point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x. This neuron will fire Poisson spikes at rate*stimulus[y,x] Hz. Brian/StimulusArrayGroup - NeuralEnsemble Cookbook - Trac