Increased firing rates monotonically expand neuronal coding bandwidth.

IF 2 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Alexander Wendt, Kolja Klett, Jan Benda, Benjamin Lindner, Jan Grewe
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引用次数: 0

Abstract

Sensory information is often processed by populations of neurons that vary with respect to their levels of spiking activity. It appears plausible that neurons that fire more often are capable of following faster changes in the input signal than those with lower firing rates. In this study, we test this intuitive assumption by systematically investigating how the spiking activity of a neuron affects information transmission. To this end, we employ the coherence function as a spectral measure of information transmission and study the relation between the width of the coherence function, which we call coding bandwidth, and the firing rate. The reexamination of experimental data from a previous study on weakly-electric fish and the simulation of biologically inspired neuron models fitted to real neurons reveal indeed a significant correlation between firing rate and coding bandwidth. In addition, known analytical expressions for the coherence function for the stochastic leaky integrate-and-fire model demonstrate a linear relation for high firing rates. However, in contrast to intuition, the width of the coherence function becomes independent of the firing rate and is completely set by the membrane time constant for very low firing rates.

放电频率的增加单调地扩大了神经元的编码带宽。
感觉信息通常是由神经元群处理的,这些神经元群根据它们的峰值活动水平而变化。比起那些放电频率较低的神经元,更频繁放电的神经元能够跟随输入信号中更快的变化,这似乎是合理的。在这项研究中,我们通过系统地研究神经元的尖峰活动如何影响信息传递来验证这一直觉假设。为此,我们采用相干函数作为信息传输的光谱度量,并研究了相干函数的宽度(我们称之为编码带宽)与发射率之间的关系。对先前对弱电鱼研究的实验数据的重新检查,以及对真实神经元的生物学启发神经元模型的模拟,确实揭示了放电速率和编码带宽之间的显著相关性。此外,已知的随机泄漏积分-射击模型的相干函数解析表达式表明,高射击率与相干函数呈线性关系。然而,与直觉相反,相干函数的宽度与发射速率无关,并且在非常低的发射速率下完全由膜时间常数决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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