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.
© 1994 Morgan Kaufmann. We thank U. Wehmeier and F. Worgotter who provided us with the code for generating the connections, and G. Holt for his retina simulator. Discussions with C. Koch and F. Wörgötter were very helpful. We would like to thank C. Koch for his continuing support and for providing a stimulating research atmosphere. We also acknowledge the Advanced Computing Laboratory of Los Alamos National Laboratory, Los Alamos, NM 87545. Some of the numerical work was performed on computing resources located at this facility. This work was supported by the National Science Foundation, the Office of Naval Research, and the Air Force Office of Scientific Research.
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