Neural
Network Simulation Environments, J. Skrzypek, ed., Kluwer, 1994, pp. 29-45.
Nexus: A Neural Simulator for Integrating Top-Down and Bottom-Up
Modeling
Paul
Sajda, Ko Sakai, Shih-Cheng Yen, and Leif H. Finkel
Department of Bioengineering and
Institute of Neurological Sciences
University of Pennsylvania
Philadelphia, PA 19104, U. S. A.
sajda@neuroengineering.upenn.edu
ko@neuroengineering.upenn.edu
syen@neuroengineering.upenn.edu
leif@neuroengineering.upenn.edu
Abstract
We have developed
the NEXUS simulation environment as a tool for modeling large-scale neural systems.
The software is written in C and runs under UNIX. A unique aspect of NEXUS is
that it is particularly suited for simulating hybrid neural models (i.e. systems
integrating different modeling paradigms and/or architectures.) NEXUS is designed
for large-scale simulations, and to facilitate model development, testing and
analysis it incorporates several major features: network architectures based
on topographic maps, programmable neural units, scalable and modular simulation,
support for common learning paradigms including the generalized Hebb rule and
backpropagation, and a user-friendly interface. These features make NEXUS a
useful environment in which to study the ``perceptual'' properties of various
network architectures.
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