Computation with biological neurons

Z. Nenadic, B. K. Ghosh

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

In this paper we discuss how an analog signal can be encoded using biophysically realistic neural networks. Such a network differs from a standard artificial neural network because of the fact that a biological cell generates spikes and information is encoded as activity of this spike generator and transmitted through a synapse between two cells. Thus a Biological Neural Network is a dynamic ensemble of cells that interact perhaps to approximate a function, perform a recursive computation such as solving a differential equation, or retain a variable in its memory. The interaction between the cells is controlled by choosing a set of synaptic weights that have to be optimized in order that a portion of the network encode a suitable function. A new optimization algorithm for finding a set of optimal synaptic weights has been proposed and successfully implemented using a software program called GENESIS. The algorithm is illustrated by implementing a memory which is a simple network of cells encoding the identify function, together with a unity feedback.

Original languageEnglish
Pages (from-to)257-262
Number of pages6
JournalProceedings of the American Control Conference
Volume1
DOIs
StatePublished - 2001
Event2001 American Control Conference - Arlington, VA, United States
Duration: Jun 25 2001Jun 27 2001

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