2023 - T1B - WS1 - Structural learning by the brain

Collection 2023 - T1B - WS1 - Structural learning by the brain

Organizer(s) Galves, Antonio ; Löcherbach, Eva ; Pouzat, Christophe ; Vargas, Claudia D.

Date(s) 06/03/2023 - 10/03/2023

linked URL https://indico.math.cnrs.fr/event/7794/

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Mathematical neuroscience

By Olivier Faugeras

Why is it important to ground neuroscience in mathematics? What kind of mathematics are relevant in this scientific area where biology, perception; action and cognition are closely intermingled? What kind of relationships should be entertained with experimentalists and computationalists? In this lecture I will try to answer these questions through examples drawn from the analysis of the activity of large populations of neurons by mathematical methods from probability, statistics, and geometry.

Information about the video

Date of recording 06/03/2023
Date of publication 06/03/2023
Institution IHP
Language English
Audience Graduate Students
Director(s) Alexandre Duplessis, Marco Perez
Format MP4
Venue Amphitheater Hermite, IHP

Citation data

DOI 10.57987/IHP.2023.T1B.WS1.001
Cite this video Faugeras, Olivier (06/03/2023). Mathematical neuroscience. IHP. Audiovisual resource. DOI: 10.57987/IHP.2023.T1B.WS1.001
URL https://dx.doi.org/10.57987/IHP.2023.T1B.WS1.001

Domain(s)

Neurons and Cognition

Bibliography

A. L. Hodgkin, A. F. Huxley / A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, vol. 117 n°4, 1952. online
E. M. Izhikevich / Dynamical Systems in Neuroscience: The Geometry of Excitability And Bursting. 2007. online
John W. Evans / Nerve Axon Equations: 1 Linear Approximations. Indiana University Mathematics Journal, vol. 21 n°9, pp. 877–885, 1972. online

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