Electrical Signaling Beyond Neurons

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Computation Pub Date : 2024-09-17 DOI:10.1162/neco_a_01696
Travis Monk;Nik Dennler;Nicholas Ralph;Shavika Rastogi;Saeed Afshar;Pablo Urbizagastegui;Russell Jarvis;André van Schaik;Andrew Adamatzky
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Abstract

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that “simpler” neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals—for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell’s assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology.
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神经元之外的电子信号传递
神经动作电位(APs)很难被解释为信号编码器和/或计算原语。神经系统本身惊人的复杂性掩盖了它们与刺激和行为之间的关系。我们可以通过观察 "更简单 "的无神经元生物,将刺激转化为影响其行为的瞬时电脉冲,从而降低这种复杂性。没有复杂的神经系统,AP 通常更容易理解为信号/反应机制。我们回顾了理论神经科学在很大程度上忽视的生命领域中的非神经刺激信号转导实例:细菌、原生动物、植物、真菌和无神经元动物。我们报告了这些电信号的特性--例如振幅、持续时间、离子基础、折射周期,尤其是它们的生态目的。我们将这些特性与神经元的特性进行比较,以推断神经元所满足的任务和选择压力。在整个生命树中,非神经刺激传导为行为对环境变化的反应定时。非神经生物以电信号的存在或不存在来表示刺激的存在或不存在。它们的信号转导通常对刺激具有高灵敏度和特异性,但与神经元相比,它们的信号转导通常比较缓慢。神经元似乎牺牲了刺激信号传导的特异性,以换取灵敏度和速度。我们将细胞刺激转导解释为细胞断言它在那一时刻检测到了重要的东西。特别是,我们将神经 AP 视为快速但有噪声的检测断言。我们推断,神经系统的主要目标是在巨大的时间压力下,从嘈杂的感觉尖峰中检测出极其微弱的信号。针对这一目标,我们讨论了神经计算建议,将神经元视为利用膜电位实现在线、模拟、概率计算的设备。这些建议意味着传入神经尖峰统计与传出神经膜电生理学之间存在可测量的关系。
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
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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