2026-09-19T11:49:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/303672022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Cadieu, Charles Fredrick
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-03-21T21:08:58Z
2006-03-21T21:08:58Z
2005
2005
http://hdl.handle.net/1721.1/30367
62232884
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 85-89).
Visual processing in biological systems is classically described as a hierarchy of increasingly sophisticated representations, originating in primary visual cortex (V1), progressing through intermediate area V4, and ascending to inferotemporal cortex. The computational mechanisms that produce representations in intermediate area V4 have remained a mystery. In this thesis I show that the standard model, a quantitative model which extends the classical description of visual processing, provides a computational mechanism capable of reproducing and predicting the responses of neurons in area V4 with a translation invariant combination of V1 responses. Using techniques I have developed, model neurons accurately predict the responses of 8 V4 neurons to within-class stimuli, such as closed contours and gratings, and achieve an average correlation coefficient of 0.77 between predicted responses and measured V4 responses. Furthermore, model neurons fit to a V4 neuron's grating stimulus response, can qualitatively predict the V4 neuron's 2-spot reverse correlation map. These results successfully demonstrate the first attempt to bridge V1 and V4 experimental data, by describing how representation in V4 could emerge from the nonlinear combination of V1 neural responses.
by Charles Fredrick Cadieu.
M.Eng.
89 p.
6101326 bytes
6330172 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Modeling shape representation in visual cortex area V4
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6f2cbe59-7d29-4655-9285-efd9cd0454ef">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Modeling shape representation in visual cortex area V4</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2005</PublicationDate>
<Authors>
<Author>
<DisplayName>Cadieu, Charles Fredrick</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Visual processing in biological systems is classically described as a hierarchy of increasingly sophisticated representations, originating in primary visual cortex (V1), progressing through intermediate area V4, and ascending to inferotemporal cortex. The computational mechanisms that produce representations in intermediate area V4 have remained a mystery. In this thesis I show that the standard model, a quantitative model which extends the classical description of visual processing, provides a computational mechanism capable of reproducing and predicting the responses of neurons in area V4 with a translation invariant combination of V1 responses. Using techniques I have developed, model neurons accurately predict the responses of 8 V4 neurons to within-class stimuli, such as closed contours and gratings, and achieve an average correlation coefficient of 0.77 between predicted responses and measured V4 responses. Furthermore, model neurons fit to a V4 neuron's grating stimulus response, can qualitatively predict the V4 neuron's 2-spot reverse correlation map. These results successfully demonstrate the first attempt to bridge V1 and V4 experimental data, by describing how representation in V4 could emerge from the nonlinear combination of V1 neural responses.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>