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Progress Made In Neuronal Dendritic Computations By SJTU Prof. Songting Li And Prof. Douglas Zhou

July 15, 2019      Author: Nie Yingyu

Recently, Songting Li, Douglas Zhou from the Insitute of Natural Sciences, and the School of Mathematical Sciences at Shanghai Jiao Tong University, together with their collaborators, developed a simple neuron model to effectively capture the dendritic functions through theoretical modeling analysis, numerical simulation, and biological experiments. The results were published online on July 10th, 2019, in the Proceedings of the National Academy of Sciences (PNAS) with the title "Dendritic computations captured by an effective point neuron model".

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The theoretical analysis and numerical simulation of the work were carried out by Prof. Songting Li, Prof. Douglas Zhou, Prof. David Cai of Shanghai Jiao Tong University and Prof. David McLaughlin of New York University. The experimental part was completed by Prof. Xiaohui Zhang from Beijing Normal University. The first author of the article is Prof. Songting Li of the Institue of Natural Sciences and the School of Mathematical Sciences, SJTU. The corresponding authors are Professor Douglas Zhou of the Institue of Natural Sciences and the School of Mathematical Sciences, SJTU, Professor David McLaughlin of New York University, and Professor Xiaohui Zhang of Beijing Normal University State Key Laboratory of Cognitive Neuroscience and Learning. The work was funded by the National Natural Science Foundation of China, etc.

 

Abstract

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

 

Translated by Iga Kowalewska       Reviewed by Wang Bingyu