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NeMo is a high-performance spiking neuralnetwork simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.
The aim of GUINNEA (Graphical User Interfaced NeuralNetwork Architecture) is to develop a comfortable and high-featured neural net simulator which is highly configurable and flexible. It will support many neural nets and visualization features for those
Multilayered feed-forward neuralnetwork software written in C++. Backpropagation and RPROP are available as training algorithms. Design goals: speed of execution when calculating the output to new data, and quality of training (preprocessing: PCA).
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Cluster Networks are a new style of neural simulation / neuralnetwork modeling, that models networks of neural populations ("clusters") that transform and transmit information using precisely-timed, graded bursts ("pulses" or "volleys") of firing.