A biophysical counting mechanism for keeping time.

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS Biological Cybernetics Pub Date : 2022-04-01 Epub Date: 2022-01-15 DOI:10.1007/s00422-021-00915-4
Klavdia Zemlianova, Amitabha Bose, John Rinzel
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引用次数: 3

Abstract

The ability to estimate and produce appropriately timed responses is central to many behaviors including speaking, dancing, and playing a musical instrument. A classical framework for estimating or producing a time interval is the pacemaker-accumulator model in which pulses of a pacemaker are counted and compared to a stored representation. However, the neural mechanisms for how these pulses are counted remain an open question. The presence of noise and stochasticity further complicates the picture. We present a biophysical model of how to keep count of a pacemaker in the presence of various forms of stochasticity using a system of bistable Wilson-Cowan units asymmetrically connected in a one-dimensional array; all units receive the same input pulses from a central clock but only one unit is active at any point in time. With each pulse from the clock, the position of the activated unit changes thereby encoding the total number of pulses emitted by the clock. This neural architecture maps the counting problem into the spatial domain, which in turn translates count to a time estimate. We further extend the model to a hierarchical structure to be able to robustly achieve higher counts.

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计时的一种生物物理计数机制。
估计和产生适当时间反应的能力是许多行为的核心,包括说话、跳舞和演奏乐器。估计或产生时间间隔的经典框架是起搏器-累加器模型,其中起搏器的脉冲被计数并与存储的表示进行比较。然而,如何计算这些脉冲的神经机制仍然是一个悬而未决的问题。噪声和随机性的存在使情况进一步复杂化。我们提出了一个生物物理模型,如何使用双稳态Wilson-Cowan单元系统在一维阵列中不对称连接,在各种形式的随机性存在下保持起搏器计数;所有单元都从中央时钟接收相同的输入脉冲,但在任何时间点只有一个单元是活动的。对于来自时钟的每个脉冲,激活单元的位置改变,从而编码时钟发出的脉冲总数。这种神经结构将计数问题映射到空间域,从而将计数转换为时间估计。我们进一步将模型扩展为层次结构,以便能够健壮地实现更高的计数。
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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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