To understand and model electrical activity in neurons and neural networks, it is necessary to solve equations on current flows. When we take a single neuron into account, we should distribute that current flow over time and space which takes us to solve a PDE (Partial differential equation) instead of a more simple ODE. Such a cable equation allowing spatial variation looks like this:
NNGPU is a GPU (CUDA) powered trainable artificial neural network simulator. It's designed to be able to manage not just multilayer perceptron (MLP) networks, but practically any kind of trainable networks. Supervised learning is to be used with backpropagation training algorithm. Ability of generalization of a network can be measured, both train and test sets can be provided. It's not a complete, fully optimized framework for making artificial neural networks; it's rather a GPGPU demonstration of speed relative to a single-threaded CPU implementation. I plan to make a comparison between (single-threaded) CPU and GPGPU run to evaluate effectiveness.
First I've made a use-case diagram on the functionality of NNGPU; here it is.
