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Information-theoretic Methods for Studying Population Codes by Robin A.A. Ince; Riccardo Senatore; Ehsan Arabzadeh; Fernando Montani; Mathew E. Diamond; Stefano Panzeri is a Computer Science article available to read on EtoBox.
What is Information-theoretic Methods for Studying Population Codes about?
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.
Who reads Information-theoretic Methods for Studying Population Codes?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Robin A.A. Ince; Riccardo Senatore; Ehsan Arabzadeh; Fernando Montani; Mathew E. Diamond; Stefano Panzeri
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)
- Published
- 2010
- Language
- EN
- Field
- Computer Science (Physical Sciences)