Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Architecture

摘要

We present a neural network simulation which we implementedudon the massively parallel Connection Machine 2. In contrast to previous work, this simulator is based on biologically realistic neurons with nontrivial single-cell dynamics, high connectivity with a structure modelled in agreement with biological data, and preservationudof the temporal dynamics of spike interactions. We simulateudneural networks of 16,384 neurons coupled by about 1000 synapses per neuron, and estimate the performance for much larger systems. Communication between neurons is identified as the computationally most demanding task and we present a novel method to overcome this bottleneck. The simulator has already been used to study the primary visual system of the cat.