Kim Lecture Hall, Rm. 1110
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Booz Allen Hamilton Distinguished Colloquium in Electrical and Computer Engineering:
"Experimental and Theoretical Analyses of Signal Propagation in Neural Networks"
Dr. Alex Reyes
Associate Professor, New York University
October 10, 2008, 2:00 p.m.
Jeong H. Kim Engineering Building, Rm. 1110
A long-standing question in neuroscience is how the nervous system encodes information. After an external stimulus is converted to electric impulses by the sensory organs, the neural signal is transmitted from neuron to neuron and from one cortical area to another. Because neurons in a given pathway have heterogeneous properties, the neural representation of the signal may undergo several transformations as it propagates through the network. Given the vast number of mostly unknown variables, a reasonable approach is to first understand the most salient features of signal processing in a relatively simple feedforward neural network. We first perform computer simulations with live neurons to show that activity in successive layers inevitably become synchronous. Next, we use Fokker-Planck equations, which were developed in the physical sciences for analyzing the behavior of large systems of randomly forced particles, to uncover the basic principles that govern the development of synchrony. Both experiments and theory indicate that synchrony is the default state of feedforward networks.
Dr. Reyes received a B.A. degree in Chemistry at the University of Chicago and a Ph.D. in Neuroscience at the University of Washington. For his first postdoc, also at U. of W., he studied the voltage-gated ions that enable neurons in the auditory brain stem to phase-lock at high frequencies and detect coincident events. He then did a 2nd postdoc at the Max Planck Institute in Heidelberg, Germany where he developed a technique for recording simultaneously from 3 neurons in a brain slice preparation. He is presently an associate professor at the Center for Neural Science in New York University. He uses both experimental and theoretical techniques to study how signals are represented in neural networks.
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