Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function parametrized to mimic f-I curves of biological neurons, either by learning an individual static function or via a learned and shared adaptation mechanism to modify activation functions in real-time during a task. We find that such adaptive networks show much-improved robustness to noise and changes in input statistics. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time. Finally, we outline similarities between these optimized solutions and known coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration.

Victor Geadah

Stefan Horoi

Giancarlo Kerg

Guy Wolf

Guillaume Lajoie

PLoS Computational Biology

PLOS 计算生物学具有特殊意义的作品，通过应用计算方法，进一步加深了我们对从分子和细胞到患者群体和生态系统的各个尺度的生命系统的理解。读者包括生命和计算科学家，他们可以将这里介绍的重要发现提升到新的发现水平。必须将研究文章声明为属于相关部分。有关这些部分的更多信息，请参见提交指南。研究文章应模拟生物系统的各个方面，展示方法和科学的新颖性，并提供深刻的新生物学见解。通常，应验证通过计算发现生物的可靠性和意义，并通过实验研究丰富。发表时不需要包含实验验证，但应尽可能引用。通过计算包含对适度生物学发现的实验验证并不能使手稿适合 PLOS 计算生物学。特别指定为方法论文的研究文章应描述已显示的具有特殊重要性的杰出方法，或有望提供新的生物学见解.该方法必须已经被广泛采用，或者有望被广大用户社区广泛采用。仅当这些增强功能带来卓越的新功能时，才会考虑对现有已发布方法的增强。

PLOS Computational Biology publishes research of significant importance that aims to deepen our understanding of life systems across various scales, from molecules and cells to patient populations and ecosystems, through the application of computational methods. The journal's readership includes life scientists and computational scientists who can leverage the important findings presented here to drive new discoveries. Research articles must clearly state their relevance to the appropriate sections, with specific requirements outlined in the submission guidelines. Articles should model various aspects of biological systems, demonstrate novelty in methods and science, and provide profound new biological insights. Typically, the reliability and significance of biological findings derived from computational approaches should be validated through experimental studies, although experimental validation is not required at the time of publication, relevant experiments should be cited whenever possible. Merely including computational validation of moderate biological findings does not qualify a manuscript for PLOS Computational Biology. Research articles designated as methods papers should describe outstanding methods that have been shown to be of special importance or have the potential to provide new biological insights. Enhancements to existing published methods will only be considered if they bring about significant new functionalities.

PLOS 计算生物学期刊发表具有重要意义的研究，旨在通过应用计算方法加深我们对生命系统的理解，涵盖从分子和细胞到患者群体和生态系统的各个尺度。该期刊的读者包括生命科学和计算科学的研究人员，他们可以利用这里的重要发现推动新的研究进展。所有研究文章必须明确声明属于相关部分，具体要求请参见提交指南。研究文章应模拟生物系统的各个方面，展示方法和科学的新颖性，并提供深刻的新生物学见解。通常情况下，计算发现的生物学可靠性和意义应通过实验研究进行验证，尽管发表时不强制要求包含实验验证，但应尽可能引用相关实验。仅通过计算验证的适度生物学发现并不足以使手稿适合发表在PLOS 计算生物学期刊上。特别指定为方法论文的研究文章应描述那些已被广泛采用或有潜力被广泛使用的杰出方法，这些方法必须具有特殊的重要性，或者能够提供新的生物学见解。只有当这些增强功能带来显著的新特性时，才会考虑对现有已发布方法的改进。

Category	Quartile
生物学	2
生物学, 生化研究方法	2
生物学, 数学与计算生物学	2

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
MATHEMATICAL & COMPUTATIONAL BIOLOGY	1