Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics

Eilon Sherman; Hedva Spitzer

doi:10.1007/3-540-45482-9_9

Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics

Eilon Sherman, Hedva Spitzer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The study aimed to establish a comprehensive computational model of intensity adaptation mechanisms, which predicts key features of experimental responses (1). We elaborated on a previous adaptation model (2) which presents retinal adaptation mechanisms and predicts responses to aperiodic stimuli. The model suggests that the temporal decline in the response of the retinal ganglion cells is a reflection of the adaptation mechanism (“curve shifting”(3)). This adaptation mechanism is applied to each cell receptive-field (RF) region (center and surround) separately, and only then the subtraction operation between the two regions is performed. The elaborated model was tested by simulating various periodic sinusoidal fields, which varied in DC level, and frequency (1-30 Hz). The model's results are in agreement with various psychophysical and physiological findings and predict most of the psychophysical key features (1). Until now, no existing model has been able to predict the key features of the experimental findings (1).

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Tomaso Poggio, Seong-Whan Lee, Heinrich H. Bulthoff
Publisher	Springer Verlag
Pages	88-97
Number of pages	10
ISBN (Print)	3540675604, 9783540675600
DOIs	https://doi.org/10.1007/3-540-45482-9_9
State	Published - 2000
Externally published	Yes
Event	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000 - Seoul, Korea, Republic of Duration: 15 May 2000 → 17 May 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1811
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000
Country/Territory	Korea, Republic of
City	Seoul
Period	15/05/00 → 17/05/00

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

Access to Document

10.1007/3-540-45482-9_9

Cite this

Sherman, E., & Spitzer, H. (2000). Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics. In T. Poggio, S.-W. Lee, & H. H. Bulthoff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 88-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_9

Sherman, Eilon ; Spitzer, Hedva. / Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Tomaso Poggio ; Seong-Whan Lee ; Heinrich H. Bulthoff. Springer Verlag, 2000. pp. 88-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d512526b4f0a40ab85ccf61d7164c623,

title = "Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics",

abstract = "The study aimed to establish a comprehensive computational model of intensity adaptation mechanisms, which predicts key features of experimental responses (1). We elaborated on a previous adaptation model (2) which presents retinal adaptation mechanisms and predicts responses to aperiodic stimuli. The model suggests that the temporal decline in the response of the retinal ganglion cells is a reflection of the adaptation mechanism (“curve shifting”(3)). This adaptation mechanism is applied to each cell receptive-field (RF) region (center and surround) separately, and only then the subtraction operation between the two regions is performed. The elaborated model was tested by simulating various periodic sinusoidal fields, which varied in DC level, and frequency (1-30 Hz). The model's results are in agreement with various psychophysical and physiological findings and predict most of the psychophysical key features (1). Until now, no existing model has been able to predict the key features of the experimental findings (1).",

author = "Eilon Sherman and Hedva Spitzer",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000 ; Conference date: 15-05-2000 Through 17-05-2000",

year = "2000",

doi = "10.1007/3-540-45482-9_9",

language = "אנגלית",

isbn = "3540675604",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "88--97",

editor = "Tomaso Poggio and Seong-Whan Lee and Bulthoff, {Heinrich H.}",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "גרמניה",

}

Sherman, E & Spitzer, H 2000, Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics. in T Poggio, S-W Lee & HH Bulthoff (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1811, Springer Verlag, pp. 88-97, 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000, Seoul, Korea, Republic of, 15/05/00. https://doi.org/10.1007/3-540-45482-9_9

Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics. / Sherman, Eilon; Spitzer, Hedva.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Tomaso Poggio; Seong-Whan Lee; Heinrich H. Bulthoff. Springer Verlag, 2000. p. 88-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics

AU - Sherman, Eilon

AU - Spitzer, Hedva

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - The study aimed to establish a comprehensive computational model of intensity adaptation mechanisms, which predicts key features of experimental responses (1). We elaborated on a previous adaptation model (2) which presents retinal adaptation mechanisms and predicts responses to aperiodic stimuli. The model suggests that the temporal decline in the response of the retinal ganglion cells is a reflection of the adaptation mechanism (“curve shifting”(3)). This adaptation mechanism is applied to each cell receptive-field (RF) region (center and surround) separately, and only then the subtraction operation between the two regions is performed. The elaborated model was tested by simulating various periodic sinusoidal fields, which varied in DC level, and frequency (1-30 Hz). The model's results are in agreement with various psychophysical and physiological findings and predict most of the psychophysical key features (1). Until now, no existing model has been able to predict the key features of the experimental findings (1).

AB - The study aimed to establish a comprehensive computational model of intensity adaptation mechanisms, which predicts key features of experimental responses (1). We elaborated on a previous adaptation model (2) which presents retinal adaptation mechanisms and predicts responses to aperiodic stimuli. The model suggests that the temporal decline in the response of the retinal ganglion cells is a reflection of the adaptation mechanism (“curve shifting”(3)). This adaptation mechanism is applied to each cell receptive-field (RF) region (center and surround) separately, and only then the subtraction operation between the two regions is performed. The elaborated model was tested by simulating various periodic sinusoidal fields, which varied in DC level, and frequency (1-30 Hz). The model's results are in agreement with various psychophysical and physiological findings and predict most of the psychophysical key features (1). Until now, no existing model has been able to predict the key features of the experimental findings (1).

UR - http://www.scopus.com/inward/record.url?scp=84958554207&partnerID=8YFLogxK

U2 - 10.1007/3-540-45482-9_9

DO - 10.1007/3-540-45482-9_9

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84958554207

SN - 3540675604

SN - 9783540675600

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 88

EP - 97

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Poggio, Tomaso

A2 - Lee, Seong-Whan

A2 - Bulthoff, Heinrich H.

PB - Springer Verlag

T2 - 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000

Y2 - 15 May 2000 through 17 May 2000

ER -

Sherman E, Spitzer H. Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics. In Poggio T, Lee SW, Bulthoff HH, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2000. p. 88-97. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45482-9_9

Confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this