Pulse-frequency-dependent resonance in a population of pyramidal neuron models.

IF 1.7 4区工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS

Biological Cybernetics Pub Date : 2022-06-01 Epub Date: 2022-03-18 DOI:10.1007/s00422-022-00925-w

Ryosuke Mori, Hiroyuki Mino, Dominique M Durand

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引用次数: 2

Abstract

Stochastic resonance is known as a phenomenon whereby information transmission of weak signal or subthreshold stimuli can be enhanced by additive random noise with a suitable intensity. Another phenomenon induced by applying deterministic pulsatile electric stimuli with a pulse frequency, commonly used for deep brain stimulation (DBS), was also shown to improve signal-to-noise ratio in neuron models. The objective of this study was to test the hypothesis that pulsatile high-frequency stimulation could improve the detection of both sub- and suprathreshold synaptic stimuli by tuning the frequency of the stimulation in a population of pyramidal neuron models. Computer simulations showed that mutual information estimated from a population of neural spike trains displayed a typical resonance curve with a peak value of the pulse frequency at 80-120 Hz, similar to those utilized for DBS in clinical situations. It is concluded that a "pulse-frequency-dependent resonance" (PFDR) can enhance information transmission over a broad range of synaptically connected networks. Since the resonance frequency matches that used clinically, PFDR could contribute to the mechanism of the therapeutic effect of DBS.

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锥体神经元模型群体中的脉冲频率相关共振。

随机共振是一种现象，通过具有适当强度的附加随机噪声可以增强弱信号或亚阈值刺激的信息传输。另一种由施加具有脉冲频率的确定性脉动电刺激引起的现象，通常用于脑深部刺激（DBS），也被证明可以提高神经元模型中的信噪比。本研究的目的是检验脉动高频刺激可以通过调节锥体神经元模型群体中的刺激频率来提高阈下和阈上突触刺激的检测的假设。计算机模拟显示，从神经棘突序列群体中估计的相互信息显示出典型的共振曲线，脉冲频率的峰值为80-120Hz，类似于临床情况下DBS所使用的那些曲线。结果表明，“脉冲频率相关共振”（PFDR）可以增强在广泛的突触连接网络上的信息传输。由于共振频率与临床使用的频率相匹配，PFDR可能有助于DBS治疗效果的机制。

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 工程技术-计算机：控制论

CiteScore

3.50

自引率

5.30%

发文量

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.