设为首页    加入收藏   联系站长   English
首页>>新闻中心

神经生物学前沿学术报告 Ole Paulsen

报告题目:Synaptic learning rules in the cortex: From spike timing-dependent to network state-dependent plasticity

报告人:   Ole Paulsen, Ph. D

Professor of Physiology, University of Cambridge,

Department of Physiology, Development and Neuroscience (PDN),

Physiological Laboratory

时间:    2019422日(周一),上午:10:30-11:30

地点:西区生物楼429会议室

主办单位:中国科学院脑功能与脑疾病重点实验室

图书馆VIP生命科学学院

合肥微尺度物质科学国家研究中心集成影像中心

报告简介:

Spike timing-dependent plasticity (STDP) has emerged as a computationally attractive learning rule for cortical circuit refinement during development. In STDP, the order and precise timing of pre- and postsynaptic action potentials determine the polarity of synaptic change: If the presynaptic input is active before the postsynaptic spike, then long-term potentiation (LTP) occurs, whereas long-term depression (LTD) is induced if this order is reversed. Both LTP and LTD require NMDA receptors, but whereas LTP always requires postsynaptic NMDA receptors, activation of presynaptic NMDA receptors may induce LTD. This raises the possibility that LTD could be induced without involvement of the postsynaptic neuron. Indeed, specific spike patterns in the presynaptic neuron can induce LTD without any requirement of postsynaptic mechanisms. That result calls for investigations into the plasticity rules that operate in vivo. Using whole-cell recordings and optogenetic stimulation of presynaptic input in urethane-anesthetized mice, which exhibit slow-wave-sleep (SWS)-like activity, we show that synaptic plasticity rules are gated by cortical dynamics: Active network states are biased towards synaptic depression, with presynaptic stimulation alone leading to LTD. This latter plasticity rule provides an attractive mechanism for SWS-related synaptic downscaling and circuit refinement. In conclusion, synaptic plasticity rules are diverse and network state-dependent.

 

附件下载

相关文档:

神经生物学前沿学术报告 Ole Paulsen

 

网站地图 - 联系我们 - 地理位置 - 人才招聘 - 资料下载 - 帮助
中国科学院脑功能与疾病重点实验室(BFD,CAS) Copyright  / all rights reserved