%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{barber:population:04:85,
         author = {Barber, David},
       projects = {Idiap},
          month = {6},
          title = {Variational Information Maximization for Population Coding},
           type = {Idiap-RR},
         number = {Idiap-RR-85-2004},
           year = {2004},
    institution = {IDIAP},
        address = {Rue de Simplon 4, Martigny, CH-1920, Switerland},
           note = {IDIAP-RR 04-85},
       abstract = {The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a long studied measure of coding efficiency, and many attempts to apply this to {\em population coding} have been made. However, this is a numerically intractable task, and most previous studies redefine the criterion in forms of an approximation to Mutual Information, the Fisher Information being one such well-known approach. Here we describe a principled bound maximisation procedure for Mutual Information learning of population codes in a simple point neural model, and compare it with other approaches.},
            pdf = {https://publications.idiap.ch/attachments/reports/2004/agakov_barber_population04_idiap_rr.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2004/agakov_barber_population04_idiap_rr.ps.gz},
ipdmembership={learning},
}