Information-theoretic methods for studying population codes

Robin A A Ince; Riccardo Senatore; Ehsan Arabzadeh; Fernando Montani; Mathew E Diamond; Stefano Panzeri

doi:10.1016/j.neunet.2010.05.008

Information-theoretic methods for studying population codes

Standard

Information-theoretic methods for studying population codes. / Ince, Robin A A; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando; Diamond, Mathew E; Panzeri, Stefano.

in: NEURAL NETWORKS, Jahrgang 23, Nr. 6, 08.2010, S. 713-27.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Review › Forschung

Harvard

Ince, RAA, Senatore, R, Arabzadeh, E, Montani, F, Diamond, ME & Panzeri, S 2010, 'Information-theoretic methods for studying population codes', NEURAL NETWORKS, Jg. 23, Nr. 6, S. 713-27. https://doi.org/10.1016/j.neunet.2010.05.008

APA

Ince, R. A. A., Senatore, R., Arabzadeh, E., Montani, F., Diamond, M. E., & Panzeri, S. (2010). Information-theoretic methods for studying population codes. NEURAL NETWORKS, 23(6), 713-27. https://doi.org/10.1016/j.neunet.2010.05.008

Vancouver

Ince RAA, Senatore R, Arabzadeh E, Montani F, Diamond ME, Panzeri S. Information-theoretic methods for studying population codes. NEURAL NETWORKS. 2010 Aug;23(6):713-27. https://doi.org/10.1016/j.neunet.2010.05.008

Bibtex

@article{edadc974d7974de1ae4cbff5a112454f,

title = "Information-theoretic methods for studying population codes",

abstract = "Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.",

keywords = "Action Potentials/physiology, Animals, Brain/cytology, Central Nervous System/cytology, Humans, Information Theory, Nerve Net/cytology, Neural Networks, Computer, Neurons/physiology",

author = "Ince, {Robin A A} and Riccardo Senatore and Ehsan Arabzadeh and Fernando Montani and Diamond, {Mathew E} and Stefano Panzeri",

year = "2010",

month = aug,

doi = "10.1016/j.neunet.2010.05.008",

language = "English",

volume = "23",

pages = "713--27",

journal = "NEURAL NETWORKS",

issn = "0893-6080",

publisher = "Elsevier Limited",

number = "6",

}

RIS

TY - JOUR

T1 - Information-theoretic methods for studying population codes

AU - Ince, Robin A A

AU - Senatore, Riccardo

AU - Arabzadeh, Ehsan

AU - Montani, Fernando

AU - Diamond, Mathew E

AU - Panzeri, Stefano

PY - 2010/8

Y1 - 2010/8

N2 - Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.

AB - Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.

KW - Action Potentials/physiology

KW - Animals

KW - Brain/cytology

KW - Central Nervous System/cytology

KW - Humans

KW - Information Theory

KW - Nerve Net/cytology

KW - Neural Networks, Computer

KW - Neurons/physiology

U2 - 10.1016/j.neunet.2010.05.008

DO - 10.1016/j.neunet.2010.05.008

M3 - SCORING: Review article

C2 - 20542408

VL - 23

SP - 713

EP - 727

JO - NEURAL NETWORKS

JF - NEURAL NETWORKS

SN - 0893-6080

IS - 6

ER -