Journal article 536 views 65 downloads
Temporal Limitations of the Standard Leaky Integrate and Fire Model
Brain Sciences, Volume: 10, Issue: 1, Start page: 16
Swansea University Author:
Joe MacInnes
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© 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/).
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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...
| Published in: | Brain Sciences |
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| ISSN: | 2076-3425 |
| Published: |
MDPI AG
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63406 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
16 |