Simulators and tools
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Encog is an advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation.
Java Bidirectional Associative Memory Neural Network | Heaton Research
Emergent
emergent is a comprehensive, full-featured neural network simulator that allows for the creation and analysis of complex, sophisticated models of the brain in the world.
Comparison of Neural Network Simulators - Emergent
From Emergent 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).Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. Python leverages a vast developer-base external to the Neuroscience community, and promises leaps in simulation complexity and maintainability to any neural simulator which adopts it. As more and more simulators support Python, model development times can be drastically reduced by promoting code sharing and reuse across simulator communities.
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
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
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 point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x.