Next Article in Journal
Understanding Social Cognition Using Virtual Reality: Are We still Nibbling around the Edges?
Previous Article in Journal
Current Therapeutic Strategies for Glioblastoma
Open AccessArticle

Temporal Limitations of the Standard Leaky Integrate and Fire Model

1
Vision Modelling Laboratory, National Research University Higher School of Economics, 109074 Moscow, Russia
2
Department of Psychology, National Research University Higher School of Economics, 101000 Moscow, Russia
3
Neuroscience and Biomedical Engineering Department, Aalto University, 02150 Espoo, Finland
4
Werner Reichardt Centre for Integrative Neuroscience, 72076 Tuebingen, Germany
5
Institute of Water Problems Russian Academy of Science, 117971 Moscow, Russia
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(1), 16; https://doi.org/10.3390/brainsci10010016
Received: 25 November 2019 / Revised: 18 December 2019 / Accepted: 19 December 2019 / Published: 27 December 2019
(This article belongs to the Section Systems Neuroscience)
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. View Full-Text
Keywords: saccade generation; salience model; visual search; leaky integrate and fire model saccade generation; salience model; visual search; leaky integrate and fire model
Show Figures

Figure 1

MDPI and ACS Style

Merzon, L.; Malevich, T.; Zhulikov, G.; Krasovskaya, S.; MacInnes, W.J. Temporal Limitations of the Standard Leaky Integrate and Fire Model. Brain Sci. 2020, 10, 16.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop