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11 - Computer Science and Informatics

University of Plymouth

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Article title

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

Type

D - Journal article

DOI

10.1371/journal.pone.0048216

Title of journal

PLOS ONE

Article number

e48216

Volume number

Issue number

First page of article

n/a

ISSN of journal

1932-6203

Year of publication

2012

URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000310741400042&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008

Number of additional authors

Additional information

<24>This paper simultaneously extends the leading computational hierarchical model (HMAX) into two crucial directions:

top-down feedback and Bayesian interpretation. It also provides approximations of belief propagation that for the first time allow to simulate 30.000 nodes in a Bayesian model. Since November 2012 the paper has already been picked up by two reviews in top-end review journals (Trends Cognitive Science; Current Opinion Neurobiology), featuring very prominently in one of them. The algorithmic efficiency allows for large Bayesian real-world applications. The first author collaborates with the Applied Physics Lab (Johns Hopkins University) to use the proposed methods in sound recognition.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract