Transistor channel dendrites implementing HMM classifiers

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number4253399
Pages (from-to)3359-3362
Journal / PublicationProceedings - IEEE International Symposium on Circuits and Systems
Publication statusPublished - 2007
Externally publishedYes

Conference

Title2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007
PlaceUnited States
CityNew Orleans, LA
Period27 - 30 May 2007

Abstract

Recently we have presented transistor channel models of biological channels and the resulting implementation towards building spiking nodes, synapses, and dendrites. We have also discussed how to build reconfigurable dendrites using programmable analog techniques. With all of this technology components available, we begin to address the question of the computation model possible using a dendrite element, as well as a network of dendrite elements. We will discuss the connection between a dendrite element and a Hidden Markov Model (HMM) classifier branch, as well as a network of dendrites and somas to create an HMM classifier typical of what is used in speech recognition systems. We present simulation and experimental results for the branch elements; we also present initial results for a small dendrite based classifier structure to show the similarities to the HMM paradigm. © 2007 IEEE.

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