Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention

Usher, Marius; Niebur, Ernst

doi:10.1162/jocn.1996.8.4.311

Cited by 147Visual perception and processing mechanismsVisual Attention and Saliency DetectionNeural dynamics and brain function

Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention

Usher, Marius, Niebur, Ernst·1996·Journal of Cognitive Neuroscience

DOI 10.1162/jocn.1996.8.4.311

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Abstract

We propose a neural model for object-oriented attention in which various visual stimuli (shapes, colors, letters, etc.) are represented by competing, mutually inhibitory, cell assemblies. The model's response to a sequence of cue and target stimuli mimics the neural responses in infero temporal (IT) visual cortex of monkeys performing a visual search task: enhanced response during the display of the stimulus, which decays but remains above a spontaneous rate after the cue disappears. When, subsequently, a display consisting of the target and several distractors is presented, the activity of all stimulus-driven cells is initially enhanced. After a short period of time, however, the activity of the cell assembly representing the cue stimulus is enhanced while the activity of the distractors decays because of mutual competition and a small top-down “expectational” input. The model fits the measured delayed activity in IT-cortex, recently reported by Chelazzi, Miller, Duncan, and Desimone (1993a), and we suggest that such a process, which is largely independent of the number of distractors, may be used by the visual system for selecting an expected target (appearing at an uncertain location) among distractors.

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In the earlier proposal, certain bottom-up pathways carried error signals indicating a mismatch between data and their reconstruction or prediction with contextual priors, and when there was no error, the lower area would be relatively inactive.
“In that proposal, certain bottom-up pathways carried error signals indicating when there was a mismatch between data and their reconstruction or prediction with contextual priors and that when there was no error, the lower area would be relatively inactive.”
From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [7]
These experiments support the earlier proposal that top-down generative signals could explain away earlier evidence based on efficient coding.
“These experiments support our earlier proposal 1 and related ideas 7,16,74 that top-down generative signals could explain away the earlier evidence based on efficient coding consideration.”
From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [7]

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Authors: Usher, M., & Niebur, E.
Affiliations: Carnegie Mellon University (US)
California Institute of Technology (US)
ORCID: Usher, Marius
Niebur, Ernst
Venue: Journal of Cognitive Neuroscience
Publisher: MIT Press - Journals
Published: 1996
Volume: 8
Issue: 4
Pages: 311-327
ISSN: 0898-929X (ISSN-L)
1530-8898
DOI: 10.1162/jocn.1996.8.4.311
OpenAlex: W2099555952
Cited by: 147
References: 68
Citations by year: 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 1 · 2014: 3 · 2013: 4 · 2012: 2
FWCI: 2.83
Type: journal article
Language: en
Open access: closed
Locations: Journal of Cognitive Neuroscience (publishedVersion)
PubMed (publishedVersion)
CaltechAUTHORS (California Institute of Technology) (submittedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords: visual search, inferotemporal cortex, cell assemblies, reciprocal inhibition, distractor suppression, temporal dynamics of neural responses

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Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention

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