BIOLOGICALLY REALISTIC AND ARTIFICIAL NEURAL NETWORK SIMULATORS ON THE CONNECTION MACHINE

Per Hammarlund (), Björn Levin () and Anders Lansner ()
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Per Hammarlund: Studies of Artificial Neural Systems, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S–100 44 Stockholm, Sweden
Björn Levin: Studies of Artificial Neural Systems, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S–100 44 Stockholm, Sweden
Anders Lansner: Studies of Artificial Neural Systems, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, S–100 44 Stockholm, Sweden

International Journal of Modern Physics C (IJMPC), 1993, vol. 04, issue 01, 49-63

Abstract: We describe two neural network (NN) simulators implemented on the Connection Machine (CM). The first program is aimed at biologically realistic simulations and the second at recurrent artificial NNs. Both programs are currently used as simulation engines in research within the SANS group as well as in other groups. The program for biologically realistic NN simulations on the CM is called BIOSIM. The aim is to simulate NNs in which the neurons are modeled with a high degree of biological realism. The cell model used is a compartmentalized abstraction of the neuron. It includes sodium, potassium, calcium, and calcium dependent potassium channels. Synaptic interaction includes conventional chemical synapses as well as voltage gated NMDA synapses. On a CM with 8K processors the program is typically capable of handling some tens of thousands of compartments and more than ten times as many synapses. The artificial NN simulator implements the SANS model, a recurrent NN model closely related to the Hopfield model. The aim has been to effectively support large network simulations, in the order of 8–16K units, on an 8K CM. To make the simulator optimal for different applications, it supports both fully and sparsely connected networks. The implementation for sparsely connected NNs uses a compacted weight matrix. Both implementations are optimized for sparse activity.

Date: 1993
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DOI: 10.1142/S0129183193000070

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