Ph.D candidate in Bioengineering
Evolving Spiking Neural Network Sensors to Characterize the Alcoholic Brain
Monday, December 2, 2013
Academic Building A G008, 5:00 PM
Co-sponsored with the EvoS GSO
The electrical activity of the brain in response to a visual stimulus can be recorded using EEG. These signals are complex spatially-distributed time series. Here we investigate if it is possible to find hidden temporal patterns in these evoked electrical signals that could characterize the alcoholic brain.
We have developed a technology for evolving spike neural network (SNN) sensors for detecting such hidden patterns in time-varying signals. The evolutionary computation involves a novel chromosome structure and a hybrid crossover operator for it. We have also developed a design rule for SNN-based temporal pattern detectors (TPD) that can detect a predefined inter-spike interval pattern in an incoming spike train. The design rule eliminates the need to tune the network parameters leaving only the design specifications to be learned. The primary goal of the evolutionary process is to select a set of EEG leads along with weights and to evolve the design specifications for the TPDs. After converting the composite EEG signal to a spike train, the TPDs are evaluated based on their ability to distinguish the alcoholic and the control cases. The early results suggest that this approach may be reliably used for characterizing the alcoholic brain.
I am a graduate student in the department of Biomedical engineering currently pursuing a Ph.D. under the guidance of Dr. J. David Schaffer and Dr. Craig B. Laramee. I have a bachelor’s degree in electronics and telecommunications from Mumbai University, Mumbai, India and a masters degree in computer science from Binghamton University, New York, U.S.A. My Ph.D. research primary involves developing strategies for evolving spike neural network (SNN) based spatio-temporal pattern classifiers (STPC) that are capable of detecting useful hidden patterns in a time varying data. Evolving such classifiers can have general applicability in a wide range of engineering areas, such as speech processing, robotics, brain machine interfacing, etc. We are currently testing the above STPC technology to characterize an alcoholic brain. My long term research goal is to understand the nature of information processing in the brain in relation to cognition and motor control and use the knowledge to develop an effective brain machine interface system.
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