Neural coding of space by time.

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-06-07 DOI:10.1007/s00422-024-00992-1
Hubert Löffler, Daya Shankar Gupta, Andreas Bahmer
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引用次数: 0

Abstract

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns.

Abstract Image

时间对空间的神经编码
空间与时间的交织构成了一项重大的科学挑战,它跨越了从哲学、物理学到神经科学的各个学科。神经编码具有内在的空间和时间维度,破译神经编码是一项复杂的任务。在本文中,我们从神经科学研究成果中汲取灵感,阐述了神经编码的时间和空间模式及其错综复杂的相互作用。我们展示了将纯粹的空间输入转换为单个尖峰序列的时间形式,并通过与锐波波纹相对应的尖峰脉冲串展示了存储、向远程位置传输和调用的过程。此外,转换后的时间表征还可以再转换成时空模式。我们使用一个简单的前馈尖峰神经网络来说明转换过程的原理。阈下膜电位振荡的频率和相位在这一框架中起着关键作用。该模型深入揭示了信息复用以及尖峰模式时间拉伸或压缩等现象。
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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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