TR available

[Arun Jagota]

The following technical report is available:

   A Hopfield-style network for content-addressable memories

                         Arun Jagota
                Department of Computer Science
            State University Of New York At Buffalo
                           90-02

                         ABSTRACT

With the binary Hopfield network as a basis, new learning and
energy descent rules are developed. It is shown, using graph
theoretic techniques, that the stable states of the network
are the maximal cliques in the underlying graph and that the
network can store an arbitrary collection of memories without
interference (memory loss, unstable fixed points). In that sense
(and that sense alone), the network has exponential capacity
(upto 2^(n/2) memories can be stored in an n-unit network).
Spurious memories can (and are likely) to develop. No analytical
results for these are derived, but important links are established 
between the storage and recall properties of the network and the 
properties of the memories that are stored. In particular it is 
shown, partly by analysing the graph underlying the network, that 
the network retrieval time and other desirable properties depend
on the 'sparse-ness' of the memories and whether they have a 
'combinatorial' structure (as defined in the report).
It is shown that the network converges in  <= n iterations and for 
sparse memories (and initial states) with sparseness k, 0 < k < n, 
it converges in <= k  iterations.
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The report is available in PostScript form by anonymous ftp as follows:

unix> ftp cheops.cis.ohio-state.edu  (or, ftp 128.146.8.62)
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose/Inbox
ftp> binary
ftp> get jagota.hsn.ps.Z
ftp> quit
unix> uncompress jagota.hsn.ps.Z
unix> lpr jagota.hsn.ps   (use flag your printer needs for Postscript) 

[sometime soon, the report may be moved to pub/neuroprose]
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It is recommended that hard-copy requests be made only if it is
not possible (or too inconvenient) to access the report via ftp.

I have developed a software simulator that I am willing to share with 
individuals who might be interested (now or later). It has been 
carefully 'tuned' for this particular model, implementing the network
algorithms in a most efficient manner. It allows configurability,
(any size 1-layer net, other parameters etc) and provides a
convenient 'symbolic' interface.

For hard-copy (and/or simulator) requests send e-mail (or write to) 
the following address. Please do not reply with 'r' or 'R' to this
message.

Arun Jagota

e-mail: jagota at cs.buffalo.edu

Dept Of Computer Science
226 Bell Hall,
State University Of New York At Buffalo,
NY - 14260