Hyperacuity in time: A CNN model of a time-coding pathway of sound localization

Károly Lotz, L. Bölöni, T. Roska, J. Hámori

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

This paper discusses a new multilayer one-dimensional (1-D) cellular neural network model of the time-coding pathway of sound localization. The key feature of the model is lateral inhibition, which is supposed to play a crucial role in sound localization. The possible role of this inhibition is examined on the basis of our model and several conclusions are drawn concerning the expected nature of inhibition. It is also shown that by use of inhibition, a group of neurons may be much more sensitive to interaural time difference than one individual neuron. Thus, our model of the first stage of the sound localization system solves a hyperacuity in time problem. The second part of the paper introduces a CNN model of that part of the sound localization system which is characterized by a massive convergence of different frequency channels to resolve the so-called phase ambiguity problem. We show that with inhibition good results can be achieved here too. Quantitative studies show the robustness of the model.

Original languageEnglish
Pages (from-to)994-1002
Number of pages9
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume46
Issue number8
DOIs
Publication statusPublished - 1999

Fingerprint

Acoustic waves
Neurons
Cellular neural networks
Multilayers

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

@article{9482e8eaee5c4a44843caa2639dda67c,
title = "Hyperacuity in time: A CNN model of a time-coding pathway of sound localization",
abstract = "This paper discusses a new multilayer one-dimensional (1-D) cellular neural network model of the time-coding pathway of sound localization. The key feature of the model is lateral inhibition, which is supposed to play a crucial role in sound localization. The possible role of this inhibition is examined on the basis of our model and several conclusions are drawn concerning the expected nature of inhibition. It is also shown that by use of inhibition, a group of neurons may be much more sensitive to interaural time difference than one individual neuron. Thus, our model of the first stage of the sound localization system solves a hyperacuity in time problem. The second part of the paper introduces a CNN model of that part of the sound localization system which is characterized by a massive convergence of different frequency channels to resolve the so-called phase ambiguity problem. We show that with inhibition good results can be achieved here too. Quantitative studies show the robustness of the model.",
author = "K{\'a}roly Lotz and L. B{\"o}l{\"o}ni and T. Roska and J. H{\'a}mori",
year = "1999",
doi = "10.1109/81.780379",
language = "English",
volume = "46",
pages = "994--1002",
journal = "IEEE Transactions on Circuits and Systems II: Express Briefs",
issn = "1057-7122",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

TY - JOUR

T1 - Hyperacuity in time

T2 - A CNN model of a time-coding pathway of sound localization

AU - Lotz, Károly

AU - Bölöni, L.

AU - Roska, T.

AU - Hámori, J.

PY - 1999

Y1 - 1999

N2 - This paper discusses a new multilayer one-dimensional (1-D) cellular neural network model of the time-coding pathway of sound localization. The key feature of the model is lateral inhibition, which is supposed to play a crucial role in sound localization. The possible role of this inhibition is examined on the basis of our model and several conclusions are drawn concerning the expected nature of inhibition. It is also shown that by use of inhibition, a group of neurons may be much more sensitive to interaural time difference than one individual neuron. Thus, our model of the first stage of the sound localization system solves a hyperacuity in time problem. The second part of the paper introduces a CNN model of that part of the sound localization system which is characterized by a massive convergence of different frequency channels to resolve the so-called phase ambiguity problem. We show that with inhibition good results can be achieved here too. Quantitative studies show the robustness of the model.

AB - This paper discusses a new multilayer one-dimensional (1-D) cellular neural network model of the time-coding pathway of sound localization. The key feature of the model is lateral inhibition, which is supposed to play a crucial role in sound localization. The possible role of this inhibition is examined on the basis of our model and several conclusions are drawn concerning the expected nature of inhibition. It is also shown that by use of inhibition, a group of neurons may be much more sensitive to interaural time difference than one individual neuron. Thus, our model of the first stage of the sound localization system solves a hyperacuity in time problem. The second part of the paper introduces a CNN model of that part of the sound localization system which is characterized by a massive convergence of different frequency channels to resolve the so-called phase ambiguity problem. We show that with inhibition good results can be achieved here too. Quantitative studies show the robustness of the model.

UR - http://www.scopus.com/inward/record.url?scp=0032642331&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032642331&partnerID=8YFLogxK

U2 - 10.1109/81.780379

DO - 10.1109/81.780379

M3 - Article

AN - SCOPUS:0032642331

VL - 46

SP - 994

EP - 1002

JO - IEEE Transactions on Circuits and Systems II: Express Briefs

JF - IEEE Transactions on Circuits and Systems II: Express Briefs

SN - 1057-7122

IS - 8

ER -