Optimization methods for spiking neurons and networks

Authors:
Alexander Russell;Garrick Orchard;Yi Dong;Ştefan Mihalaş;Ernst Niebur;Jonathan Tapson;Ralph Etienne-Cummings
Affiliations:
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD;Zanvyl-Krieger Mind Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD;Zanvyl-Krieger Mind Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD;Zanvyl-Krieger Mind Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD;Department of Electrical Engineering, University of Cape Town, Rondebosch, South Africa;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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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.