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Computational role of disinhibition in brain functionYu, Yingwei 02 June 2009 (has links)
Neurons are connected to form functional networks in the brain. When neurons are
combined in sequence, nontrivial effects arise. One example is disinhibition; that is,
inhibition to another inhibitory factor. Disinhibition may be serving an important
purpose because a large number of local circuits in the brain contain disinhibitory
connections. However, their exact functional role is not well understood.
The objective of this dissertation is to analyze the computational role of disinhibition
in brain function, especially in visual perception and attentional control.
My approach is to propose computational models of disinhibition and then map the
model to the local circuits in the brain to explain psychological phenomena. Several
computational models are proposed in this dissertation to account for disinhibition.
(1) A static inverse difference of Gaussian filter (IDoG) is derived to account explicitly
for the spatial effects of disinhibition. IDoG can explain a number of complex
brightness-contrast illusions, such as the periphery problem in the Hermann grid and
the White's effect. The IDoG model can also be used to explain orientation perception
of multiple lines as in the modified version of Poggendorff illusion. (2) A
spatio-temporal model (IDoGS) in early vision is derived and it successfully explains
the scintillating grid illusion, which is a stationary display giving rise to a striking,
dynamic, scintillating effect. (3) An interconnected Cohen-Grossberg neural network
model (iCGNN) is proposed to address the dynamics of disinhibitory neural networks with a layered structure. I derive a set of sufficient conditions for such an interconnected
system to reach asymptotic stability. (4) A computational model combining
recurrent and feed-forward disinhibition is designed to account for input-modulation
in temporal selective attention.
The main contribution of this research is that it developed a unified framework of
disinhibition to model several different kinds of neural circuits to account for various
perceptual and attentional phenomena. Investigating the role of disinhibition in the
brain can provide us with a deeper understanding of how the brain can give rise to
intelligent and complex functions.
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