摘要：

The interplay between excitatory and inhibitory inputs imparts rich computations of a neuron in the brain. To understand synaptic mechanisms underlying neuronal computation, a fundamental approach is to recover the dynamics of excitatory and inhibitory synaptic inputs to a target neuron. Mathematically, it is an inverse problem to infer the input synaptic conductance from the output neural activity, which remains challenging. In this talk, we first propose the concept of effective conductance that is proportional to the local input conductance on the dendrites and reflects directly the synaptic impact on spike generation. By performing asymptotic analysis on the cable model, we further develop a framework to determine the effective conductance reliably. Furthermore, we develop a method to recover the dynamics of local conductance on the dendrites. The effectiveness of our methods has been verified in both realistic neuron simulations and electrophysiological experiments. Therefore, our methods overcome the challenge of the space clamp effect and present a reliable assessment of the role of synaptic activity in neuronal computation.

简介：

I am a Tenure-track Associate Professor in the Institute of Natural Sciences and the School of Mathematical Sciences at Shanghai Jiao Tong University working on applied mathematics and computational neuroscience. I received my B.S. and Ph.D. degrees in Mathematics from Shanghai Jiao Tong University in 2010 and 2014 respectively, and my M.S. degree in Industrial Engineering from the Georgia Institute of Technology in 2015. Before joining Shanghai Jiao Tong University, I worked as a Postdoc in the Courant Institute of Mathematical Sciences at New York University from 2015 to 2018.

摘要：

The interplay between excitatory and inhibitory inputs imparts rich computations of a neuron in the brain. To understand synaptic mechanisms underlying neuronal computation, a fundamental approach is to recover the dynamics of excitatory and inhibitory synaptic inputs to a target neuron. Mathematically, it is an inverse problem to infer the input synaptic conductance from the output neural activity, which remains challenging. In this talk, we first propose the concept of effective conductance that is proportional to the local input conductance on the dendrites and reflects directly the synaptic impact on spike generation. By performing asymptotic analysis on the cable model, we further develop a framework to determine the effective conductance reliably. Furthermore, we develop a method to recover the dynamics of local conductance on the dendrites. The effectiveness of our methods has been verified in both realistic neuron simulations and electrophysiological experiments. Therefore, our methods overcome the challenge of the space clamp effect and present a reliable assessment of the role of synaptic activity in neuronal computation.

简介：

I am a Tenure-track Associate Professor in the Institute of Natural Sciences and the School of Mathematical Sciences at Shanghai Jiao Tong University working on applied mathematics and computational neuroscience. I received my B.S. and Ph.D. degrees in Mathematics from Shanghai Jiao Tong University in 2010 and 2014 respectively, and my M.S. degree in Industrial Engineering from the Georgia Institute of Technology in 2015. Before joining Shanghai Jiao Tong University, I worked as a Postdoc in the Courant Institute of Mathematical Sciences at New York University from 2015 to 2018.