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Excitatory versus inhibitory feedback in Bayesian formulations of scene construction

Abadi, AK; Yahya, K; Amini, M; Friston, K; Heinke, D; (2019) Excitatory versus inhibitory feedback in Bayesian formulations of scene construction. Journal of the Royal Society Interface , 16 (154) , Article 20180344. 10.1098/rsif.2018.0344. Green open access

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Abstract

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

Type: Article
Title: Excitatory versus inhibitory feedback in Bayesian formulations of scene construction
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsif.2018.0344
Publisher version: https://doi.org/10.1098/rsif.2018.0344
Language: English
Additional information: Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: selective visual attention, computational modelling, active inference, parallel distributed processing, neuroimaging
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10074150
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