NDLI: Optimization Methods for Spiking Neurons and Networks

Content Provider	IEEE Xplore Digital Library
Author	Russell, A. Orchard, G. Yi Dong Mihalaş, S. Niebur, E. Tapson, J. Etienne-Cummings, R.
Copyright Year	1990
Abstract	Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron's output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas-Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip.
Sponsorship	IEEE Computational Intelligence Society
Page Count	13
File Size	1029527
Starting Page	1950
Ending Page	1962
File Format	PDF
ISSN	10459227
Volume Number	21
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Mathematical model Integrated circuit modeling Biomembranes Artificial neural networks Robots Biological system modeling Genetic algorithms spiking neuron Genetic algorithm maximum likelihood optimization
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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