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SLONN: A Simulation Language for modeling of Neural Networks

Brain Simulation Laboratory Computer Science Department University of Southern California Los Angeles, CA 90089-0782, USA

Chochun Hsu

Department of Computer Science and Technology Peking University, Beijing, P.R.China

This paper presents a general purpose Simulation Language for modeling Of Neural Networks (SLONN) which has been implemented in our laboratory. Based on a new neuron model, SLONN can represent both spatial and temporal summation of a single neuron and synaptic plasticity. By introducing fork to describe a connection pattern between neurons and by using repetition connec tion, module type and module array to specify large networks, SLONN can be used to specify both small and large neural networks effectively. This language is distinguished by its hierarchical organiza tion, which makes it possible to catch very general aspects at higher levels as well as very specific properties at lower levels. As an example to demonstrate some features of SLONN, we have modeled the habitua tion and sensitization behaviors in Aplysia.

Key Words: artificial neural networks • connection pattern • empirical neuron model • habituation • leaky integrator model • learning rule • module array • module type • neural modeling • neural networks • simulation language • SLONN.

SIMULATION, Vol. 55, No. 2, 69-83 (1990)
DOI: 10.1177/003754979005500203


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