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 withnontrivial single-cell dynamics, high connectivity with a structure modelled in agreement with biological data, and preservation ofthe 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 mostdemanding task and we present a novel method to overcome thisbottleneck. 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 (Fujimotoet al., 1992, Zhang et al., 1990). However, these are implementations ofartificial, highly simplified neural networks, while our aim was explicitly to provide a simulator for biologically realistic neural networks. There is also at least one implementation of biologically realistic neuronal systems on a moderately 904 Efficient Simulation of Biological Neural Networks 905 parallel but powerful machine (De Schutter and Bower, 1992), but the complexity of the used neuron model makes simulation of larger numbers of neurons impractical. Ourinterest here is to provide an efficient simulator of large neural networks of cortex and related subcortical structures. The most important characteristics of the neuronal systems we want to simulate are the following: - Cells are highly interconnected (several thousand connections per cell) but far from fully interconnected.