[FONT=Times New Roman, Times, serif]Computational and Mathematical Models of Memory[/FONT]
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One of the distinguishing features of the Brain Sciences Program at Brown University is the unusually close and frequent interaction of brain theorists with bench experimentalists. Although the utility of theoretical arguments is well established in the physical sciences, with a few notable exceptions, the blending of theory and experiment in neuroscience has been challenging. Researchers at Brown have been at the forefront of developing theoretical models that have proved invaluable in elucidating the connections between molecular and cellular events mediating learning and memory. One of these projects, for example, that developed from a collaboration of Nobel Laureate Leon Cooper and Applied Mathematics/Neuroscience Professor Elie Bienenstock has led to a theory of synaptic plasticity (the BCM theory),which applied to a simple model of the visual cortex and the visual environment, explains how experience shapes the development of the visual system and determines its ultimate wiring pattern. The BCM theory has also sparked considerable experimental studies to show how synapses know when to increase or decrease their strength. The theoretical work on learning and memory has served to provide a deeper understanding of the physiology underlying learning and memory. Work in the laboratories of Professors James Anderson and Harel Shouval are examining the theoretical foundations of learning using simulations and models that incorporate artificial intelligence and statistics to develop adaptive machines that can take advantage of observations and examples in order to solve a variety of tasks that are achieved easily by human nervous systems, but poorly by computers.
The combined efforts of theoretical and experimental researchers in the Brain Science Program provide a unique approach to both understanding the nature of human learning and memory and the biological mechanisms that allow us to learn and remember.[/FONT]
