Adaptive face space models with dynamic neural priors and sparse coding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visual adaptation leads to certain changes in the perceived identity of a face depending on the previous faces an observer has been exposed to. The explicit effect of adaptation on face perception is not well understood. This work presents, for what is believed to be the first time, a mathematical model to capture the effect of adaptation on face identity perception. Our model is grounded in three assumptions that are consistent with recent neurobiological findings: (1) adaptation has the highest effect on the neural activities generated at the lowest levels of visual cortex; (2) neural activities in low-level visual areas are representatives of extracted features from a stimulus face, and as a result, encoding the stimulus face in the face space is affected by the adaptation process; and (3) a stimulus face is represented by a small number of simultaneously active neurons out of a large population (sparse coding). The rate of target identification with or without adaptation obtained by the proposed computational model resembles psyhometric functions obtained in prior studies using real subjects.

LanguageEnglish
Title of host publicationICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages389-392
Number of pages4
Volume2018-January
ISBN (Electronic)9781538619117
DOIs
Publication statusPublished - Feb 14 2018
Externally publishedYes
Event24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia
Duration: Dec 5 2017Dec 8 2017

Conference

Conference24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017
CountryGeorgia
CityBatumi
Period12/5/1712/8/17

Fingerprint

Neurons
Mathematical models

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Reihanian, S., Zollanvari, A., & James, A. P. (2018). Adaptive face space models with dynamic neural priors and sparse coding. In ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems (Vol. 2018-January, pp. 389-392). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECS.2017.8292008

Adaptive face space models with dynamic neural priors and sparse coding. / Reihanian, Samira; Zollanvari, Amin; James, Alex Pappachen.

ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 389-392.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reihanian, S, Zollanvari, A & James, AP 2018, Adaptive face space models with dynamic neural priors and sparse coding. in ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 389-392, 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017, Batumi, Georgia, 12/5/17. https://doi.org/10.1109/ICECS.2017.8292008
Reihanian S, Zollanvari A, James AP. Adaptive face space models with dynamic neural priors and sparse coding. In ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 389-392 https://doi.org/10.1109/ICECS.2017.8292008
Reihanian, Samira ; Zollanvari, Amin ; James, Alex Pappachen. / Adaptive face space models with dynamic neural priors and sparse coding. ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 389-392
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