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

We present a neural network simulation which we implemented on 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 preservation of the temporal dynamics of spike interactions. We simulate neural 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. 1 INTRODUCTION Neural networks have been implemented previously on massively parallel supercomputers (Fujimoto et al., 1992, Zhang et al., 1990). However, these are implementations of artificial, highly simplified neural networks, while our aim was explicitly to provide a simulator for biologically realistic neural networks.