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Temporal Limitations of the Standard Leaky Integrate and Fire Model

Liya Merzon, Tatiana Malevich, Georgiy Zhulikov, Sofia Krasovskaya, Joe MacInnes Orcid Logo

Brain Sciences, Volume: 10, Issue: 1, Start page: 16

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.3390/brainsci10010016

Abstract

Itti and Koch’s Saliency Model has been used extensively to simulate fixation selection in a variety of tasks from visual search to simple reaction times. Although the Saliency Model has been tested for its spatial prediction of fixations in visual salience, it has not been well tested for their tem...

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Published in: Brain Sciences
ISSN: 2076-3425
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa63406
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spelling v2 63406 2023-05-11 Temporal Limitations of the Standard Leaky Integrate and Fire Model 06dcb003ec50192bafde2c77bef4fd5c 0000-0002-5134-1601 Joe MacInnes Joe MacInnes true false 2023-05-11 SCS Itti and Koch’s Saliency Model has been used extensively to simulate fixation selection in a variety of tasks from visual search to simple reaction times. Although the Saliency Model has been tested for its spatial prediction of fixations in visual salience, it has not been well tested for their temporal accuracy. Visual tasks, like search, invariably result in a positively skewed distribution of saccadic reaction times over large numbers of samples, yet we show that the leaky integrate and fire (LIF) neuronal model included in the classic implementation of the model tends to produce a distribution shifted to shorter fixations (in comparison with human data). Further, while parameter optimization using a genetic algorithm and Nelder–Mead method does improve the fit of the resulting distribution, it is still unable to match temporal distributions of human responses in a visual task. Analysis of times for individual images reveal that the LIF algorithm produces initial fixation durations that are fixed instead of a sample from a distribution (as in the human case). Only by aggregating responses over many input images do they result in a distribution, although the form of this distribution still depends on the input images used to create it and not on internal model variability. Journal Article Brain Sciences 10 1 16 MDPI AG 2076-3425 1 1 2020 2020-01-01 10.3390/brainsci10010016 http://dx.doi.org/10.3390/brainsci10010016 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2023-06-08T15:10:33.8298696 2023-05-11T11:29:51.3397763 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Liya Merzon 1 Tatiana Malevich 2 Georgiy Zhulikov 3 Sofia Krasovskaya 4 Joe MacInnes 0000-0002-5134-1601 5 63406__27647__4b266b12cec64fb7aa627ba07abd7bd4.pdf 63406.pdf 2023-05-31T09:59:37.1079431 Output 2961619 application/pdf Version of Record true © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). true eng http://creativecommons.org/licenses/by/4.0/
title Temporal Limitations of the Standard Leaky Integrate and Fire Model
spellingShingle Temporal Limitations of the Standard Leaky Integrate and Fire Model
Joe MacInnes
title_short Temporal Limitations of the Standard Leaky Integrate and Fire Model
title_full Temporal Limitations of the Standard Leaky Integrate and Fire Model
title_fullStr Temporal Limitations of the Standard Leaky Integrate and Fire Model
title_full_unstemmed Temporal Limitations of the Standard Leaky Integrate and Fire Model
title_sort Temporal Limitations of the Standard Leaky Integrate and Fire Model
author_id_str_mv 06dcb003ec50192bafde2c77bef4fd5c
author_id_fullname_str_mv 06dcb003ec50192bafde2c77bef4fd5c_***_Joe MacInnes
author Joe MacInnes
author2 Liya Merzon
Tatiana Malevich
Georgiy Zhulikov
Sofia Krasovskaya
Joe MacInnes
format Journal article
container_title Brain Sciences
container_volume 10
container_issue 1
container_start_page 16
publishDate 2020
institution Swansea University
issn 2076-3425
doi_str_mv 10.3390/brainsci10010016
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.3390/brainsci10010016
document_store_str 1
active_str 0
description Itti and Koch’s Saliency Model has been used extensively to simulate fixation selection in a variety of tasks from visual search to simple reaction times. Although the Saliency Model has been tested for its spatial prediction of fixations in visual salience, it has not been well tested for their temporal accuracy. Visual tasks, like search, invariably result in a positively skewed distribution of saccadic reaction times over large numbers of samples, yet we show that the leaky integrate and fire (LIF) neuronal model included in the classic implementation of the model tends to produce a distribution shifted to shorter fixations (in comparison with human data). Further, while parameter optimization using a genetic algorithm and Nelder–Mead method does improve the fit of the resulting distribution, it is still unable to match temporal distributions of human responses in a visual task. Analysis of times for individual images reveal that the LIF algorithm produces initial fixation durations that are fixed instead of a sample from a distribution (as in the human case). Only by aggregating responses over many input images do they result in a distribution, although the form of this distribution still depends on the input images used to create it and not on internal model variability.
published_date 2020-01-01T15:10:32Z
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score 11.013148

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