New Neuroprose Entry

[Fry, Robert L.]

     A preprint of a manuscript entitled "Observer-participant models of 
neural computation" which has been accepted by the IEEE Trans. Neural 
Networks is being made available via FTP from the Neuroprose directory. 
 Details for retrieving this article an associated figures follows the 
abstract below:

               ABSTRACT

     Observer-participant models of neural computation

          A model is proposed in which the neuron serves as an information 
channel. Channel distortion occurs through the channel since the mapping 
from input boolean codes to output codes are many-to-one in that neuron 
outputs consist of just two distinguished states. Within the described 
model, the neuron performs a decision-making function.  Decisions are made 
regarding the validity of a question passively posed by the neuron to its 
environment.  This question becomes defined through learning hence learning 
is viewed as the process of determining an appropriate question based on 
supplied input ensembles.  An application of the Shannon information 
measures of entropy and mutual information taken together in the context of 
the proposed model lead to the Hopfield neuron model with conditionalized 
Hebbian learning rules implemented through a simple modification to Oja's 
learning equation.  Neural decisions are shown to be based on a  sigmoidal 
transfer characteristic or in the limit as computational temperature tends 
to zero, a maximum likelihood decision rule.  The described work is 
contrasted with the information-theoretic approach of Linsker.


The paper is available in two files from archive.cis.ohio-state.edu in the
/pub/neuroprose  subdirectory.  The file names are

     fry.maxmut.ps.Z   (compressed postscript manuscript 433927 bytes)
     fry.maxmut_figs.ps.Z (comp. postscript figures 622321 bytes)

Robert L. Fry
Johns Hopkins University/
Applied Physics Laboratory
Johns Hopkins Road
Laurel, MD 20723
robert_fry at jhuapl.edu