Webb, Tristan J. (2013) Biologically plausible attractor networks. PhD thesis, University of Warwick.

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Abstract

Attractor networks have shownmuch promise as a neural network architecture
that can describe many aspects of brain function. Much of the field of study
around these networks has coalesced around pioneering work done by John
Hoprield, and therefore many approaches have been strongly linked to the field
of statistical physics. In this thesis I use existing theoretical and statistical notions
of attractor networks, and introduce several biologically inspired extensions
to an attractor network for which a mean-field solution has been previously
derived. This attractor network is a computational neuroscience model
that accounts for decision-making in the situation of two competing stimuli.
By basing our simulation studies on such a network, we are able to study situations where mean-
field solutions have been derived, and use these as the starting
case, which we then extend with large scale integrate-and-fire attractor network
simulations. The simulations are large enough to provide evidence that the results
apply to networks of the size found in the brain. One factor that has been
highlighted by previous research to be very important to brain function is that
of noise. Spiking-related noise is seen to be a factor that influences processes
such as decision-making, signal detection, short-term memory, and memory
recall even with the quite large networks found in the cerebral cortex, and this
thesis aims to measure the effects of noise on biologically plausible attractor
networks. Our results are obtained using a spiking neural network made up
of integrate-and-fire neurons, and we focus our results on the stochastic transition
that this network undergoes. In this thesis we examine two such processes
that are biologically relevant, but for which no mean-field solutions yet
exist: graded firing rates, and diluted connectivity. Representations in the cortex
are often graded, and we find that noise in these networks may be larger than
with binary representations. In further investigations it was shown that diluted
connectivity reduces the effects of noise in the situation where the number of
synapses onto each neuron is held constant. In this thesis we also use the same
attractor network framework to investigate the Communication through Coherence
hypothesis. The Communication through Coherence hypothesis states
that synchronous oscillations, especially in the gamma range, can facilitate communication
between neural systems. It is shown that information transfer from
one network to a second network occurs for a much lower strength of synaptic
coupling between the networks than is required to produce coherence. Thus,
information transmission can occur before any coherence is produced. This indicates
that coherence is not needed for information transmission between coupled
networks. This raises a major question about the Communication through
Coherence hypothesis. Overall, the results provide substantial contributions
towards understanding operation of attractor neuronal networks in the brain.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH):	Neural networks (Neurobiology), Attractors (Mathematics), Neural networks (Computer science)
Official Date:	March 2013
Dates:	Date Event March 2013 Submitted
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Feng, Jianfeng; Rolls, Edmund T.
Extent:	xx, 108 leaves : illustrations.
Language:	eng

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