The mammalian brain is a complex computing system that contains billions of neurons and trillions of connections. Is there a general principle that governs the processing in such large neural populations? This dissertation attempts to address this question using computational modeling and quantitative analysis of direct physiological measurements of large neural populations in the monkey primary visual cortex (V1). First, the complete spatiotemporal dynamics of V1 responses over the entire region that is activated by small stationary stimuli are characterized quantitatively. The dynamics of the responses are found to be systematic but complex. Importantly, they are inconsistent with many popular computational models of neural processing. Second, a simple population gain control (PGC) model that can account for these complex response properties is proposed for the small stationary stimuli. The PGC model is then used to predict the responses to stimuli composed of two elements and stimuli that move at a constant speed. The predictions of the model are consistent with the measured responses in V1 for both stimuli. PGC is the first model that can account for the complete spatiotemporal dynamics of V1 population responses for different types of stimuli, suggesting that gain control is a general mechanism of neural processing.

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PhD Thesis, Department of Computer Sciences, University of Texas at Austin. Technical Report AI09-06.

@PhDThesis{sit:phd09, title={A Population Gain Control Model of Spatiotemporal Responses in the Visual Cortex}, author={Yiu Fai Sit}, school={Department of Computer Sciences, University of Texas at Austin}, address={Austin, TX}, institution={Department of Computer Sciences, University of Texas at Austin}, note={Technical Report AI09-06}, url="http://www.cs.utexas.edu/users/ai-lab?sit:phd09", year={2009} }