University of Hertfordshire

Lateral inhibitory networks: Synchrony, edge enhancement, and noise reduction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • C. Glackin
  • L. Maguire
  • L. McDaid
  • J. Wade
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Original languageEnglish
Title of host publicationProcs of the 2011 International Joint Conference on Neural Networks
Subtitle of host publication(IJCNN)
PublisherIEEE
Pages1003-1009
Number of pages7
ISBN (Print)978-1-4244-9637-2
DOIs
Publication statusPublished - 2011
Event2011 Int Joint Conf on Neural Networks IJCNN - San Jose, United States
Duration: 31 Jul 20115 Aug 2011

Conference

Conference2011 Int Joint Conf on Neural Networks IJCNN
CountryUnited States
CitySan Jose
Period31/07/115/08/11

Abstract

This paper investigates how layers of spiking neurons can be connected using lateral inhibition in different ways to bring about synchrony, reduce noise, and extract or enhance features. To illustrate the effects of the various
connectivity regimes spectro-temporal speech data in the form
of isolated digits is employed. The speech samples are preprocessed
using the Lyon’s Passive Ear cochlear model, and then encoded into tonotopically arranged spike arrays using the BSA spiker algorithm. The spike arrays are then subjected to various lateral inhibitory connectivity regimes configured
by two connectivity parameters, namely connection length and
neighbourhood size. The combination of these parameters are
demonstrated to produce various effects such as transient
synchrony, reduction of noisy spikes, and sharpening of spectrotemporal
features

ID: 736887