计算神经科学系列讲座（三）-- Gating and Information Processing in Feedforward Networks

报告时间：2020-12-10  20:00-21:00

报告人：陶乐天 研究员  北京大学

会议地点：zoom会议室  ID：615 5727 0903

主办单位：国家天元数学中部中心

组委会：黄橙橙 (匹兹堡大学), 张继伟 (武汉大学), 周栋焯 (上海交通大学)

Abstract：Neural oscillations can enhance feature recognition, and improve learning and memory. Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks. Unanswered questions are: 1) what is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Here we show example circuits that can perform a variety of functions, including learning and memory transfer.