Computational Role of Disinhibition in Brain Function

COMPUTATIONAL ROLE OF DISINHIBITION IN BRAIN FUNCTION. (August 2006) Yingwei Yu, B.E., Beihang University, China Chair of Advisory Committee: Dr. Yoonsuck Choe Neurons are connected to form functional networks in the brain. When neurons are combined in sequence, nontrivial effects arise. One example is disinhibition, 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) was derived to explicitly account for the spatial effects of disinhibition. IDoG can explain a number of complex brightnesscontrast 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 was derived and it successfully explained 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) was proposed to address the dynamics of disinhibitory neural networks with a layered