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兴奋性突触整合的线性化无需额外代价。

Linearization of excitatory synaptic integration at no extra cost.

作者信息

Morel Danielle, Singh Chandan, Levy William B

机构信息

Physics Department, Emory & Henry College, Emory, VA, 24327, USA.

Departments of Neurosurgery and of Psychology, University of Virginia, Charlottesville, VA, 22904, USA.

出版信息

J Comput Neurosci. 2018 Apr;44(2):173-188. doi: 10.1007/s10827-017-0673-5. Epub 2018 Jan 25.

Abstract

In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation. Different combinations of voltage-gated conductances were examined, and many are found to produce linearization; here, four of these models are presented. Comparing the energy-costs to a purely passive model, reveals minimal or even no additional costs in some cases.

摘要

在许多神经计算理论中,线性求和的突触激活是单个神经元执行计算时普遍存在的假设。实际上,对于某些名义上的最优模型,需要线性求和。然而,产生线性求和所需的生物物理机制可能会增加神经处理的能量成本。因此,线性求和带来的好处可能会被能量成本所抵消。本文使用相对简单的神经元模型中的电压门控电导,量化了使树突局部突触激活线性化的成本。研究了电压门控电导的不同组合,发现其中许多组合会产生线性化;这里展示了其中四个模型。将能量成本与纯被动模型进行比较,发现在某些情况下成本最小甚至没有额外成本。

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