Title Information
Title
Visual Recognition with a Large Scale Network of Dynamical Systems
Name: Personal
Name Part
Dimitriadis, Socrates
Role
Role Term: Text
creator
Origin Information
Copyright Date
2010
Physical Description
Extent
xiii, 145 p.
digitalOrigin
born digital
Note
Thesis (Ph.D. -- Brown University (2010)
Name: Personal
Name Part
Anderson, James
Role
Role Term: Text
Director
Name: Personal
Name Part
Domini, Fulvio
Role
Role Term: Text
Reader
Name: Personal
Name Part
Serre, Thomas
Role
Role Term: Text
Reader
Name: Corporate
Name Part
Brown University. Cognitive and Linguistic Sciences: Cognitive Sciences
Role
Role Term: Text
sponsor
Type of Resource
text
Genre (aat)
theses
Abstract
In this thesis we begin to articulate a computational theory that provides a neuronal-leveldescription of the macro-level function of the visual system, and we propose a particularinstantiation of it that is based on a biologically plausible large scale integration of dynamicalsystems. Inspired by the columnar organization of the visual cortex and the brain's immenseassociative power, we suggest a framework composed of a very large number of small autoassociativeneural networks that create network assemblies by hetero-associating their internalstates. The constituent networks, as abstractions of cortical columns that become selective tolocal receptive fields, form associative memories of the respective image patches. At the sametime, these networks are linked with each other and form lateral and hierarchical assembliesthat become selective to complex configurations of whole visual percepts. We believe that thiscognitive fusion of the dynamical states at various scales, is the basis for high level visualrecognition. In order to face the immense computational demands of our approach, we modelthe proposed framework as a parallel distributed system and we conduct our experiments with ahigh performance computing cluster. Experimental results demonstrate that the proposedframework can model a variety of behavioral data related to visual cognition in humans. Theinherent use of dynamics, in combination with the versatile associativity, make it very suitablefor modeling the temporal aspects of visual recognition and provide testable predictionsregarding the interplay of time with the neuronal connectivity.
Subject
Topic
visual
Subject
Topic
recognition
Subject
Topic
neural
Subject
Topic
network
Subject
Topic
dynamical
Subject
Topic
system
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1167852")
Topic
Vision
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20111003
Language
Language Term: Code (ISO639-2B)
eng
Language Term: Text
English
Identifier: DOI
10.7301/Z07942XS