Recurrent Competition Explains Temporal Effects of Attention in MSTd

Navigation in a static environment along straight paths without eye movements produces radial optic flow fields. A singularity called the focus of expansion (FoE) specifies the direction of travel (heading) of the observer. Cells in primate visual area MSTd respond to radial fields and are therefore...

Full description

Bibliographic Details
Main Authors: Oliver W. Layton, N. Andrew eBrowning
Format: Article
Language:English
Published: Frontiers Media S.A. 2012-10-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00080/full
id doaj-fdee4abf4b9841088f04c49bd4687359
record_format Article
spelling doaj-fdee4abf4b9841088f04c49bd46873592020-11-24T20:41:24ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882012-10-01610.3389/fncom.2012.0008024822Recurrent Competition Explains Temporal Effects of Attention in MSTdOliver W. Layton0N. Andrew eBrowning1Boston UniversityBoston UniversityNavigation in a static environment along straight paths without eye movements produces radial optic flow fields. A singularity called the focus of expansion (FoE) specifies the direction of travel (heading) of the observer. Cells in primate visual area MSTd respond to radial fields and are therefore thought to be heading-sensitive. Humans frequently shift their focus of attention while navigating, for example, depending on the favorable or threatening context of approaching independently moving objects. Recent neurophysiological studies show that the spatial tuning curves of primate MSTd neurons change based on the difference in visual angle between an attentional prime and the FoE. Moreover, the peak mean population activity in MSTd retreats linearly in time as the distance between the attentional prime and FoE increases. We present a dynamical neural circuit model that demonstrates the same linear temporal peak shift observed electrophysiologically. The model qualitatively matches the neuron tuning curves and population activation profiles. After model MT dynamically pools short-range motion, model MSTd incorporates recurrent competition between units tuned to different radial optic flow templates, and integrates attentional signals from model area FEF. In the model, population activity peaks occur when the recurrent competition is most active and uncertainty is greatest about the relative position of the FoE. The nature of attention, multiplicative or non-multiplicative, is largely irrelevant, so long as attention has a Gaussian-like profile. Using an appropriately tuned sigmoidal signal function to modulate recurrent feedback affords qualitative fits of deflections in the population activity that otherwise appear to be low-frequency noise. We predict that these deflections mark changes in the balance of attention between the priming and FoE locations.http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00080/fullAttentionOptic FlownavigationModelmotion processingHeading
collection DOAJ
language English
format Article
sources DOAJ
author Oliver W. Layton
N. Andrew eBrowning
spellingShingle Oliver W. Layton
N. Andrew eBrowning
Recurrent Competition Explains Temporal Effects of Attention in MSTd
Frontiers in Computational Neuroscience
Attention
Optic Flow
navigation
Model
motion processing
Heading
author_facet Oliver W. Layton
N. Andrew eBrowning
author_sort Oliver W. Layton
title Recurrent Competition Explains Temporal Effects of Attention in MSTd
title_short Recurrent Competition Explains Temporal Effects of Attention in MSTd
title_full Recurrent Competition Explains Temporal Effects of Attention in MSTd
title_fullStr Recurrent Competition Explains Temporal Effects of Attention in MSTd
title_full_unstemmed Recurrent Competition Explains Temporal Effects of Attention in MSTd
title_sort recurrent competition explains temporal effects of attention in mstd
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2012-10-01
description Navigation in a static environment along straight paths without eye movements produces radial optic flow fields. A singularity called the focus of expansion (FoE) specifies the direction of travel (heading) of the observer. Cells in primate visual area MSTd respond to radial fields and are therefore thought to be heading-sensitive. Humans frequently shift their focus of attention while navigating, for example, depending on the favorable or threatening context of approaching independently moving objects. Recent neurophysiological studies show that the spatial tuning curves of primate MSTd neurons change based on the difference in visual angle between an attentional prime and the FoE. Moreover, the peak mean population activity in MSTd retreats linearly in time as the distance between the attentional prime and FoE increases. We present a dynamical neural circuit model that demonstrates the same linear temporal peak shift observed electrophysiologically. The model qualitatively matches the neuron tuning curves and population activation profiles. After model MT dynamically pools short-range motion, model MSTd incorporates recurrent competition between units tuned to different radial optic flow templates, and integrates attentional signals from model area FEF. In the model, population activity peaks occur when the recurrent competition is most active and uncertainty is greatest about the relative position of the FoE. The nature of attention, multiplicative or non-multiplicative, is largely irrelevant, so long as attention has a Gaussian-like profile. Using an appropriately tuned sigmoidal signal function to modulate recurrent feedback affords qualitative fits of deflections in the population activity that otherwise appear to be low-frequency noise. We predict that these deflections mark changes in the balance of attention between the priming and FoE locations.
topic Attention
Optic Flow
navigation
Model
motion processing
Heading
url http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00080/full
work_keys_str_mv AT oliverwlayton recurrentcompetitionexplainstemporaleffectsofattentioninmstd
AT nandrewebrowning recurrentcompetitionexplainstemporaleffectsofattentioninmstd
_version_ 1716825269412560896