Title: Compressed Sensing Coding in Neuronal Network Dynamics

Speaker: 周栋焯 (Douglas Zhou)

Institute of Natural Sciences & School of Mathematical Sciences, Shanghai Jiao Tong University

时间：2017年7月20日下午3:00-4:00

地点： 理科楼407

Abstract：

Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of this sparsity? We explore this question and show that significant downstream reductions in the numbers of neurons transmitting stimuli observed in early sensory pathways might be a consequence of this sparsity. First, we model an early sensory pathway using an idealized neuronal network comprised of receptors and downstream sensory neurons.  Then, by revealing a linear structure intrinsic to neuronal network dynamics, our work points to a potential mechanism for transmitting sparse stimuli, related to compressed-sensing (CS) type data acquisition. Through simulation, we examine the characteristics of networks that are optimal in sparsity encoding, and the impact of localized receptive fields beyond conventional CS theory. The results of this work suggest a new network framework of signal sparsity, freeing the notion from any dependence on specific component space representations. We expect our CS network mechanism to provide guidance for studying sparse stimulus transmission along realistic sensory pathways as well as engineering network designs that utilize sparsity encoding.

报告人简介：

周栋焯博士于2002和2007年在北京大学获学士和博士学位，从2007年至2009年，他在美国纽约大学库朗研究所从事博士后研究，周栋焯博士于2010年加入上海交通大学自然科学研究院、数学科学学院，从2010年1月至2016年1月任特别研究员，从2016年2月至今任教授。周栋焯博士目前的主要研究兴趣是理论和计算神经科学领域的科学问题，包括神经元网络动力学的信息编码原理的研究，针对神经生理实验现象的数学建模与动力学模拟以及神经元网络机制的研究，发展有助实验的数据处理方法等，其工作发表在如PNAS，PLoS Comput. Biol，Phys. Rev. Lett.以及J. Comput. Neurosci.等国际期刊上。