Literature DB >> 19125356

Modelling attention in individual cells leads to a system with realistic saccade behaviours.

Linda J Lanyon1, Susan L Denham.   

Abstract

Single cell recordings in monkey inferior temporal cortex (IT) and area V4 during visual search tasks indicate that modulation of responses by the search target object occurs in the late portion of the cell's sensory response (Chelazzi et al. in J Neurophysiol 80:2918-2940, 1998; Cereb Cortex 11:761-772, 2001) whereas attention to a spatial location influences earlier responses (Luck et al. in J Neurophysiol 77:24-42, 1997). Previous computational models have not captured differences in the latency of these attentional effects and yet the more protracted development of the object-based effect could have implications for behaviour. We present a neurodynamic biased competition model of visual attention in which we aimed to model the timecourse of spatial and object-based attention in order to simulate cellular responses and saccade onset times observed in monkey recordings. In common with other models, a top-down prefrontal signal, related to the search target, biases activity in the ventral visual stream. However, we conclude that this bias signal is more complex than modelled elsewhere: the latency of object-based effects in V4 and IT, and saccade onset, can be accurately simulated when the target object feedback bias consists of a sensory response component in addition to a mnemonic response. These attentional effects in V4 and IT cellular responses lead to a system that is able to produce search scan paths similar to those observed in monkeys and humans, with attention being guided to locations containing behaviourally relevant stimuli. This work demonstrates that accurate modelling of the timecourse of single cell responses can lead to biologically realistic behaviours being demonstrated by the system as a whole.

Entities:  

Year:  2009        PMID: 19125356      PMCID: PMC2727161          DOI: 10.1007/s11571-008-9073-x

Source DB:  PubMed          Journal:  Cogn Neurodyn        ISSN: 1871-4080            Impact factor:   5.082


  74 in total

1.  Population dynamics of spiking neurons: fast transients, asynchronous states, and locking.

Authors:  W Gerstner
Journal:  Neural Comput       Date:  2000-01       Impact factor: 2.026

2.  Visual search is facilitated by scene and sequence familiarity in rhesus monkeys.

Authors:  Daeyeol Lee; Stephan Quessy
Journal:  Vision Res       Date:  2003-06       Impact factor: 1.886

3.  Voluntary orienting is dissociated from target detection in human posterior parietal cortex.

Authors:  M Corbetta; J M Kincade; J M Ollinger; M P McAvoy; G L Shulman
Journal:  Nat Neurosci       Date:  2000-03       Impact factor: 24.884

4.  The guidance of eye movements during active visual search.

Authors:  B C Motter; E J Belky
Journal:  Vision Res       Date:  1998-06       Impact factor: 1.886

5.  Responses of neurons in inferior temporal cortex during memory-guided visual search.

Authors:  L Chelazzi; J Duncan; E K Miller; R Desimone
Journal:  J Neurophysiol       Date:  1998-12       Impact factor: 2.714

6.  Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area.

Authors:  C L Colby; J R Duhamel; M E Goldberg
Journal:  J Neurophysiol       Date:  1996-11       Impact factor: 2.714

7.  The representation of visual salience in monkey parietal cortex.

Authors:  J P Gottlieb; M Kusunoki; M E Goldberg
Journal:  Nature       Date:  1998-01-29       Impact factor: 49.962

8.  Superior parietal cortex activation during spatial attention shifts and visual feature conjunction.

Authors:  M Corbetta; G L Shulman; F M Miezin; S E Petersen
Journal:  Science       Date:  1995-11-03       Impact factor: 47.728

9.  Covert orienting of attention in macaques. II. Contributions of parietal cortex.

Authors:  D L Robinson; E M Bowman; C Kertzman
Journal:  J Neurophysiol       Date:  1995-08       Impact factor: 2.714

10.  Neural correlates of feature selective memory and pop-out in extrastriate area V4.

Authors:  B C Motter
Journal:  J Neurosci       Date:  1994-04       Impact factor: 6.167

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  6 in total

1.  Neuro-robotics study on integrative learning of proactive visual attention and motor behaviors.

Authors:  Sungmoon Jeong; Hiroaki Arie; Minho Lee; Jun Tani
Journal:  Cogn Neurodyn       Date:  2011-10-08       Impact factor: 5.082

2.  A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level.

Authors:  Tao Zhang; Xiaochuan Pan; Xuying Xu; Rubin Wang
Journal:  Cogn Neurodyn       Date:  2019-05-23       Impact factor: 5.082

3.  Modelling visual neglect: computational insights into conscious perception.

Authors:  Linda J Lanyon; Susan L Denham
Journal:  PLoS One       Date:  2010-06-15       Impact factor: 3.240

4.  Neural network modelling of the influence of channelopathies on reflex visual attention.

Authors:  Alexandre Gravier; Chai Quek; Włodzisław Duch; Abdul Wahab; Joanna Gravier-Rymaszewska
Journal:  Cogn Neurodyn       Date:  2015-11-09       Impact factor: 5.082

5.  Predicting the eye fixation locations in the gray scale images in the visual scenes with different semantic contents.

Authors:  Hassan Zanganeh Momtaz; Mohammad Reza Daliri
Journal:  Cogn Neurodyn       Date:  2015-10-07       Impact factor: 5.082

6.  Visual search and line bisection in hemianopia: computational modelling of cortical compensatory mechanisms and comparison with hemineglect.

Authors:  Linda J Lanyon; Jason J S Barton
Journal:  PLoS One       Date:  2013-02-04       Impact factor: 3.240

  6 in total

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