Simulators and tools
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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.
Welcome to our comparison of neural network simulators. We welcome your contributions in the form of adding new simulators (see the link to the right) and editing simulators in order to keep their information up to date and accurate (see the 'edit this simulator' link below each simulator). You may also recommend new comparison columns in the table by adding a note on the Discussion page (see the Discussion tab in the upper left).
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.
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.
So far we've looked at how to simulate a simple LIF model neuron and a complex Hodgkin-Huxley model neuron .
This code is for creating a group of neurons which fire with a given 2D stimulus at a given rate.