by C.J. Rozell and D.H. Johnson
Abstract:
Mutual information enjoys wide use in the computational neuroscience community for analyzing spiking neural systems. Its direct calculation is difficult because estimating the joint stimulus-response distribution requires a prohibitive amount of data. Consequently, several techniques have appeared for bounding mutual information that rely on less data. We examine two upper bound techniques and find that they are either unreliable or introduce strong assumptions about the neural code. We also examine two lower bounds, showing that they can be very loose and possibly bear little relation to the mutual information's actual value.
Reference:
Examining methods for estimating mutual information in spiking neural systemsC.J. Rozell and D.H. Johnson. Neurocomputing, vol. 65–66C, pp. 429–434, June 2005.
Bibtex Entry:
@ARTICLE{rozell.04c,
author = {Rozell, C.J. and Johnson, D.H.},
title = {Examining methods for estimating mutual information in spiking neural systems},
journal = {Neurocomputing},
year = {2005},
volume = {65--66C},
pages = {429--434},
month = {June},
abstract = {Mutual information enjoys wide use in the computational neuroscience community for
analyzing spiking neural systems. Its direct calculation is difficult
because estimating the joint stimulus-response distribution requires a
prohibitive amount of data. Consequently, several techniques have
appeared for bounding mutual information that rely on less data. We
examine two upper bound techniques and find that they are either
unreliable or introduce strong assumptions about the neural code. We also
examine two lower bounds, showing that they can be very loose and
possibly bear little relation to the mutual information's actual value.},
url = {http://siplab.gatech.edu/pubs/rozellCNS2004.pdf},
}