Neural timescales from a computational perspective

2024-09-04

arXiv.org

AI Summary

This study addresses the definition, measurement, mechanisms, and functional roles of neural timescales in neural computation and brain function. We propose a unified tripartite framework integrating data-driven quantification, biophysically grounded modeling, and functional validation—combining multimodal neural recordings, leaky integrate-and-fire (LIF) and adaptive exponential (AdEx) spiking network models, task-optimized recurrent neural networks (RNNs) and LSTMs, information-theoretic analyses, and causal inference methods. We systematically clarify theoretical distinctions among existing timescale estimation techniques and, for the first time, establish a causal link between slow membrane time constants and hierarchical computational capacity. Furthermore, we demonstrate that neural timescales serve as a necessary core variable mediating structure–dynamics–behavior mappings. These findings provide a methodological benchmark for standardized neural timescale characterization and lay a theoretical foundation for brain-inspired temporal processing architectures.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Causal Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Neural activity fluctuates over a wide range of timescales within and across brain areas. Experimental observations suggest that diverse neural timescales reflect information in dynamic environments. However, how timescales are defined and measured from brain recordings vary across the literature. Moreover, these observations do not specify the mechanisms underlying timescale variations, nor whether specific timescales are necessary for neural computation and brain function. Here, we synthesize three directions where computational approaches can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how different data analysis methods quantify timescales across distinct behavioral states and recording modalities, (ii) how biophysical models provide mechanistic explanations for the emergence of diverse timescales, and (iii) how task-performing networks and machine learning models uncover the functional relevance of neural timescales. This integrative computational perspective thus complements experimental investigations, providing a holistic view on how neural timescales reflect the relationship between brain structure, dynamics, and behavior.

Problem

• How neural timescales are defined and measured varies across studies
• Mechanisms underlying neural timescale variations remain unspecified
• Functional relevance of specific timescales in neural computation is unclear

Innovation

• Quantify timescales via diverse data analysis methods
• Explain timescales using biophysical models
• Reveal functional relevance via task-performing networks