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Computational methods to study information processing in neural circuits

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Computational methods to study information processing in neural circuits. / Koren, Veronika; Bondanelli, Giulio; Panzeri, Stefano.

In: COMPUT STRUCT BIOTEC, Vol. 21, 11.01.2023, p. 910-922.

Research output: SCORING: Contribution to journal › SCORING: Review article › Research

Harvard

Koren, V, Bondanelli, G & Panzeri, S 2023, 'Computational methods to study information processing in neural circuits', COMPUT STRUCT BIOTEC, vol. 21, pp. 910-922. https://doi.org/10.1016/j.csbj.2023.01.009

APA

Koren, V., Bondanelli, G., & Panzeri, S. (2023). Computational methods to study information processing in neural circuits. COMPUT STRUCT BIOTEC, 21, 910-922. https://doi.org/10.1016/j.csbj.2023.01.009

Vancouver

Koren V, Bondanelli G, Panzeri S. Computational methods to study information processing in neural circuits. COMPUT STRUCT BIOTEC. 2023 Jan 11;21:910-922. https://doi.org/10.1016/j.csbj.2023.01.009

Bibtex

@article{e3a9b245e3a74a99a9afead6a60ceed5,
title = "Computational methods to study information processing in neural circuits",
abstract = "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.",
author = "Veronika Koren and Giulio Bondanelli and Stefano Panzeri",
note = "{\textcopyright} 2023 The Authors.",
year = "2023",
month = jan,
day = "11",
doi = "10.1016/j.csbj.2023.01.009",
language = "English",
volume = "21",
pages = "910--922",
journal = "COMPUT STRUCT BIOTEC",
issn = "2001-0370",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Computational methods to study information processing in neural circuits

AU - Koren, Veronika

AU - Bondanelli, Giulio

AU - Panzeri, Stefano

N1 - © 2023 The Authors.

PY - 2023/1/11

Y1 - 2023/1/11

N2 - 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.

AB - 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.

U2 - 10.1016/j.csbj.2023.01.009

DO - 10.1016/j.csbj.2023.01.009

M3 - SCORING: Review article

C2 - 36698970

VL - 21

SP - 910

EP - 922

JO - COMPUT STRUCT BIOTEC

JF - COMPUT STRUCT BIOTEC

SN - 2001-0370

ER -