Dawei W. Dong RESEARCH STATEMENT My research emphasizes finding fundamental theories that explain how the nervous system codes and uses sensory information. The approach is based on ideas of optimal coding from information theory, for example, decorrelation of sensory inputs to make statistically independent representations. The basic assumption is that such representation of sensory signals facilitates the performance of perception and cognition and gives an evolution advantage to the brain. Assuming that the brain utilizes such optimal codings, one can in principle predict the neural processing. Making such prediction involves: (1) measuring the statistical properties of sensory signals in an animal's natural environment, and then, (2) mathematically deriving the optimal transforms for those sensory signals. The predicted transforms can then be compared with the actual processing observed in physiological and psychophysical experiments. The visual sense provides unique opportunities for highly structured inputs, of which the statistical properties can be quantitatively measured, and to which neural responses have been extensively studied. Therefore, I often test my theories on the visual system. In particular, I test my theories on the visual systems of humans and of other vertebrates whose visual systems share key features of evolutionary kinship with humans and are believed to be highly optimized for information processing. I am now particularly interested in the dynamic/non-stationary aspects of vision. In collaboration with other scientists, I am engaged in a number of related research topics: * Statistics of natural images. In order to understand how the visual system processes input information, one needs to understand more about the properties of visual inputs under natural conditions. I have collected many samples of natural time-varying images and analyzed their spatiotemporal statistics. This work has helped the currently emerging understanding of the statistics of spatiotemporal correlations and their relation to motions of the observer relative to visual scenes. Further more, we take eye-movements into account and investigate their effect on the image statistics and other non-stationary properties of the visual input. * Optimal coding. From the measured properties of natural time-varying images, the spatial-temporal coding of the lateral geniculate nucleus (LGN), a processing network between retina and cortex, can be predicted; the predictions are in quantitative agreement with available physiological data and have been used to guide new experiments. In particular, I predict the dynamic nature of the LGN responses, i.e., dependence on the saccade timings and scenes. I have also predicted quantitatively the perceptual shifts caused by adaptation to different sensory input structures, in particular, the orientation illusions; these predicted shifts, which are believed to have cortical origin, are consistent with recent psychophysical and physiological data. * Learning dynamics. It is of great interests to study how neural networks self-organize to learn the desired optimal coding under natural conditions. The goal here is to find convergent, biologically plausible development algorithms that are driven by neuronal inputs and outputs. I have found one such algorithm to generate optimal information processing networks. In simulation, when given natural time-varying images, this algorithm produces the spatial-temporal receptive fields of early visual pathways. Current simulations, with both feedforward and feedback connections, produce the columnar organization observed in striate cortex. * Biophysical model. We investigate if well-known ion channel properties can facilitate information-theoretic optimal coding through temporal decorrelation; and if so, whether the degree of temporal decorrelation can be adapted dynamically to ensure such optimization at longer time scales. We found that dynamic decorrelation is obtainable by varying temporal filtering through changing the resting membrane potential, for example, the biophysical properties of LGN cells support the role of temporal decorrelation and enable a plausible feedback control mechanism when input statistics change. * Brain imaging. We are developing a new method of functional MRI study of brain activities and integrating eye-tracking into MRI experiments. We compare the information processing during two states achieved by `free viewing' vs. `forced viewing' of natural time-varying images. During the `free viewing', a subject is shown a segment of a natural movie and will view it with his/her own natural eye movements. In the `forced viewing' conditions the subject will fixate at a cross on the screen and the movie will be played back to the subject with the shifts in space and time corresponding to the subject's eye movements during one of the `free viewing' trials, so that the resulting retinal visual input will be the same. We found different activities for the two conditions in both sensory visual areas and ocular motor areas. * Neurophysiological experiment. The single unit activities of LGN cells in a awake and free viewing cat are recorded during and between watching natural time varying images. Simultaneously, the eye movement of cat is recorded. The recorded eye positions are used to derive the actual visual input on the retina. The auto-correlation of the the spike train and its cross-correlation with retinal input are calculated to reveal the STRF properties. We find that The spike train is temporally decorrelated not only on the average but also for different saccade timings and scenes. We showed that both right before/after and between saccades, the information transfer through LGN is optimized during natural viewing. * Neurological experiment. We tracked the eye movements of Parkinsons Disease (PD) patients during free-viewing natural time-varying images. Among the PD patients we have access to are those that are receiving deep-brain stimulation (DBS) to the subthalamic nucleus and inner sector of the globus pallidus. We measured the statistics of eye movements in these individuals, with and without DBS, and compared them with normal controls, free-viewing the same natural time-varying images. We showed that under natural viewing conditions, the basal ganglia play a crucial role in regulating and perhaps initiating the saccadic eye movements. The statistics of the eye movements, including the fixation time and the number and the amplitude of saccades, might be used as sensitive indicators of PD status and the basal ganglia function.