Koren Veronika, Bondanelli Giulio, Panzeri Stefano
Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany.
Istituto Italiano di Tecnologia, Via Melen 83, Genova 16152, Italy.
Comput Struct Biotechnol J. 2023 Jan 11;21:910-922. doi: 10.1016/j.csbj.2023.01.009. eCollection 2023.
The brain is an information processing machine and thus naturally lends itself to be studied using computational tools based on the principles of information theory. For this reason, computational methods based on or inspired by information theory have been a cornerstone of practical and conceptual progress in neuroscience. In this Review, we address how concepts and computational tools related to information theory are spurring the development of principled theories of information processing in neural circuits and the development of influential mathematical methods for the analyses of neural population recordings. We review how these computational approaches reveal mechanisms of essential functions performed by neural circuits. These functions include efficiently encoding sensory information and facilitating the transmission of information to downstream brain areas to inform and guide behavior. Finally, we discuss how further progress and insights can be achieved, in particular by studying how competing requirements of neural encoding and readout may be optimally traded off to optimize neural information processing.
大脑是一台信息处理机器，因此自然而然地适合使用基于信息论原理的计算工具来进行研究。出于这个原因，基于信息论或受其启发的计算方法一直是神经科学在实践和概念方面取得进展的基石。在本综述中，我们探讨了与信息论相关的概念和计算工具如何推动神经回路中信息处理原理性理论的发展，以及如何推动用于分析神经群体记录的有影响力的数学方法的发展。我们回顾了这些计算方法如何揭示神经回路执行基本功能的机制。这些功能包括有效地编码感官信息，并促进信息向下游脑区的传递，以告知和指导行为。最后，我们讨论如何取得进一步的进展和见解，特别是通过研究神经编码和读出的相互竞争需求如何能够得到最佳权衡，以优化神经信息处理。
