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Progress Made In Neuron Dendritic Computations By SJTU Prof. Li Songting And Zhou Dongzhuo

July 23, 2019      Author: Nie Yingyu

Recently, Li Songting, Zhou Dongzhuo of School of Natural Sciences, School of Mathematical Sciences, Shanghai Jiao Tong University, and collaborators, developed a simple neuron model through theoretical modeling analysis, numerical simulation, and biological experiments, which constitutes as an effective description of nerve dendritic computing functio. 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.

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


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