1. 제 목 : Evolution of Recollection and Prediction in Neural Networks
2. 발 표 자 : Yoonsuck Choe(Texas A&M University, 부교수)
3. 일 시 : 2009년 7월 29일 (수요일) / 11:00(AM) ~ 13:00(PM)
4. 장 소 : 공대 10호관 313호
5. 초청교수 : 이 민호 교수
6. 강사약력
Yoonsuck Choe is an associate professor
Director of the Brain Networks Laboratory in the Department of Computer Science and Engineering at
Texas A&M University.
He received his B.S. degree in Computer Science from Yonsei University(Korea) in 1993,
his M.S. and Ph.D. degrees in Computer Sciences from the University of Texas at Austin in 1995 and 2001.
His current research areas include computational neuroscience, neural networks, computational neuroanatomy,
neuroinformatics, and biologically motivated vision.
7. 내용요약 :
A large number of neural network models are based on a feedforward topology (perceptrons, backpropagation networks, radial basis functions, support vector machines, etc.), thus lacking dynamics. In such networks, the order of input presentation is meaningless (i.e., it does not affect the behavior) since the behavior is largely reactive. That is, such neural networks can only operate in the present, having no access to the past or the future. However, biological neural networks are mostly constructed with a recurrent topology, and recurrent (artificial) neural network models are able to exhibit rich temporal dynamics, thus time becomes an essential factor in their operation. In this paper, we will investigate the emergence of recollection and prediction in evolving neural networks. First, we will show how reactive, feedforward networks can evolve a memory-like function (recollection) through utilizing external markers dropped and detected in the environment. Second, we will investigate how recurrent networks with more predictable internal state trajectory can emerge as an eventual winner in evolutionary struggle when competing networks with less predictable trajectory show the same level of behavioral performance. We expect our results to help us better understand the evolutionary origin of recollection and prediction in neuronal networks, and better appreciate the role of time in brain function.
8. 주최 : 전자전기컴퓨터학부, BK21 정보기술연구인력양성사업단,
센서기술연구소
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