HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Nemenman, I. Search for Related Content PubMed PubMed Citation Articles by Nemenman, I.

Fluctuation-Dissipation Theorem and Models of Learning

ilya.nemenman{at}columbia.edu, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, and Joint Centers for Systems Biology, Columbia University, New York, NY 10032, U.S.A.

Advances in statistical learning theory have resulted in a multitude of different designs of learning machines. But which ones are implemented by brains and other biological information processors? We analyze how various abstract Bayesian learners perform on different data and argue that it is difficult to determine which learning–theoretic computation is performed by a particular organism using just its performance in learning a stationary target (learning curve). Based on the fluctuation–dissipation relation in statistical physics, we then discuss a different experimental setup that might be able to solve the problem.

This article has been cited by other articles:

				S. Cheng and P. N. Sabes Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics J Neurophysiol, April 1, 2007; 97(4): 3057 - 3069. [Abstract] [Full Text] [PDF]