% Bibliography from the book "Introduction to the Theory of Neural
% Computation" by John Hertz, Anders Krogh, and Richard Palmer
% (Addison-Wesley, 1991)
%
% After a suggestion from Tali Tishby we decided to make the bibliography
% for our book publicly available in the Neuroprose Archive.  The copyright
% of the book is owned by Addison-Wesley and this bibliography is placed in
% the public domain with their permission.  We spent considerable effort on
% the bibliography while writing the book, and hope that other researchers
% will benefit from it.
%
% It is written in the TeX format developed for the book, and this file
% includes the macros needed to make it TeX-able.  It should be fairly easy
% to adapt the macros and/or bibliographic entries for individual needs.
% Nici Schraudolph has made a BibTeX version of this file and placed it in
% the Neuroprose archive in the file hertz.refs.bib.Z. If anyone improves
% it in other ways, we encourage them to put the new version in the
% Neuroprose Archive, or e-mail it to one of the following addresses so
% that we can do so.
%
%              askrogh@nbivax.nbi.dk
%              palmer@phy.duke.edu
%
% Please note that we are not intending to update this bibliography, except
% to correct mistakes.  It would be great if someone would maintain a complete
% online NN bibliography, but WE cannot.  So please don't send us requests of
% the form "please add my paper ...".
%
% This file was updated May 14 1991.  A few minor corrections were made.
%
%                          John Hertz, Anders Krogh, and Richard G. Palmer.
%
% ----------------------------------------------------------------------
%                              Legal Stuff
%
% In agreeing to place this bibliography in the public domain, Addison-
% Wesley asked that we include copyright information and ordering information
% for the book from which it is taken.  This information should be retained
% with the bibliography:


\centerline{\bf BIBLIOGRAPHY}  
\smallskip
\centerline{from the book} 
\smallskip
\centerline{\sl Introduction to the Theory of Neural Computation}
\smallskip
\centerline{by}
\smallskip
\centerline{John Hertz, Anders Krogh, and Richard Palmer}
\smallskip
\centerline{(Addison-Wesley, 1991)}

\medskip
Addison-Wesley holds the copyright on this material, but gives permission
for free individual use.  This does {\it not\/} cover any form of redistribution
for sale or profit.  Contact Addison-Wesley directly (e-mail
{\tt D4065@applelink.apple.com}) for more information.

The book itself may be ordered directly from Addison-Wesley in the U.S. at
1--800--447--2226 (toll-free) or by mail from {\it Addison-Wesley, The Advanced
Book Program, 350 Bridge Parkway, Suite 209, Redwood City, CA 94065, U.S.A}. 
The price is \$29.95 paperbound (ISBN 
0--201--51560--1) or \$44.25 hardbound (ISBN 0--201--50395--6).  Please quote
the ISBN in your order and add local sales tax plus \$2.00 (in the U.S.) or
\$5.00 (overseas airmail) for shipping and handling charges.

\bigskip
%
%----------------------------------------------------------------------
%  References are organized as follows:
%
%  Papers, preprints, reports:
%      \paper
%      {Author(s) and year}
%      {Title}
%      {journal etc}
%
%  Books:
%      \book
%      {Author(s) and year}
%      {Title}
%      {city: publisher}
%
%  Articles in books:
%      \paper
%      {Author(s) and year}
%      {Title}
%      {\inbook{title}{location}{editors}{volume}{pages}{city: publisher}}
%
% Any of arguments 2-6 of \inbook may be empty:
% e.g. \inbook{title}{}{}{}{100-110}{}
% Many of the more common conferences have individual macros with one
% or two arguments (pages, or volume, pages) that define an \inbook.
%
% A period should not be included at the end of any entry; one is added
% by the macros if necessary.  \noperiod is put at the end of an entry
% if a period should NOT be added (e.g. if a title ends with a "?")
%
% \journ{journalname}{volume}{page--page} is used for journal references.
% Argument 2 or 3 may be empty.  The journalname is always defined by a
% macro -- see the list below.


% ----------------------------- general layout -------------------------------
\raggedright \tolerance=2000 \hbadness=1500
\parindent=0pt \parskip=2pt \baselineskip=11pt
\everypar={\hangindent=6mm\hangafter=1\ignorespaces}%
\def\par{\endgraf\vfil\penalty-200\vfilneg}%
\headline={\hfil\ifnum \pageno>1\tenbf Hertz--Krogh--Palmer Bibliography\fi}


% ----------------- macros for bibliographic entries -----------------------
%
\newif\ifperiod\periodtrue
\def\noperiod{\periodfalse}
\def\period{\ifperiod .\else\periodtrue\fi}
\def\paper#1#2#3{\periodtrue #1\period\space #2\period\space #3\period}
\def\book#1#2#3{\periodtrue #1\period\space {\sl #2}\period\space #3\period}
\def\inbook#1#2#3#4#5#6{\periodtrue
   In {\sl #1}%
   \def\testarg{#2}\ifx\testarg\empty\else \/\ (#2)\fi
   \def\testarg{#3}\ifx\testarg\empty\else , #3\fi 
   \def\testarg{#4}\ifx\testarg\empty\else , #4\fi 
   \def\testarg{#5}\ifx\testarg\empty\else , \nohyphenbreak{#5}\fi
   \def\testarg{#6}\ifx\testarg\empty\else \period\space #6\fi}
\def\journ#1#2#3{{\sl #1}%
   \def\testarg{#2}\ifx\testarg\empty\else \/\ {\bf #2}\fi
   \def\testarg{#3}\ifx\testarg\empty\else , \nohyphenbreak{#3}\fi}
\def\nohyphenbreak#1{\setbox0=\hbox{#1}\unhbox0}

% miscellaneous useful abbreviations
\def\ed{ed.\ }
\def\eds{eds.\ }
\def\etal{et al.}

% ------------------------------- Journals ---------------------------------
\def\applopt{Applied Optics}
\def\annphys{Annals of Physics}
\def\bbs{Behavioral and Brain Sciences}
\def\behbio{Behavioral Biology}
\def\biocyb{Biological Cybernetics}
\def\brain{Brain}
\def\bmbiol{Bulletin of Mathematical Biology}
\def\bmbiophys{Bulletin of Mathematical Biophysics}
\def\BYTE{BYTE}
\def\cogsci{Cognitive Science}
\def\computer{Computer}
\def\connsci{Connection Science}
\def\cs{Complex Systems}
\def\cpc{Computer Physics Communications}
\def\cspla{Computer Speech and Language}
\def\cvgip{Computer Vision, Graphics, and Image Processing}
\def\daed{D\ae dalus, Proceedings of the American Academy of Arts
   and Sciences}
\def\EEGCN{EEG and Clinical Neurophysiology}
\def\eul{Europhysics Letters}
\def\febsl{FEBS Letters}
\def\ieeeassp{IEEE ASSP Magazine}
\def\ieeesmc{IEEE Transactions on Systems, Man, and Cybernetics}
\def\ieeeit{IEEE Transactions on Information Theory}
\def\ieeetassp{IEEE Transactions on Acoustics, Speech, and Signal
   Processing}
\def\ieeetcomm{IEEE Transactions on Communications}
\def\ieeetc{IEEE Transactions on Computers}
\def\ieeetcas{IEEE Transactions on Circuits and Systems}
\def\ieeetec{IEEE Transactions on Electronic Computers}
\def\ieeetpami{IEEE Transactions on Pattern Analysis and Machine
   Intelligence}
\def\ijns{International Journal of Neural Systems}
\def\jama{Journal of Mathematical Analysis and Applications}
\def\jasa{Journal of the Acoustical Society of America}
\def\jcomp{Journal of Complexity}
\def\jcp{Journal of Chemical Physics}
\def\jmathb{Journal of Mathematical Biology}
\def\jmolecb{Journal of Molecular Biology}
\def\jmp{Journal of Mathematical Physics}
\def\jmpsych{Journal of Mathematical Psychology}
\def\jneuro{Journal of Neuroscience}
\def\jpa{Journal of Physics A}
\def\jphysiol{Journal of Physiology (London)}
\def\jpp{Journal de Physique (Paris)}
\def\jppl{Journal de Physique Lettres (Paris)}
\def\jtb{Journal of Theoretical Biology}
\def\kyb{Kybernetik}
\def\mlearn{Machine Learning}
\def\mbio{Mathematical Biosciences}
\def\mcss{Mathematics of Control, Signals, and Systems}
\def\nature{Nature}
\def\network{Network}
\def\nc{Neural Computation}
\def\nn{Neural Networks}
\def\nnsupp{Neural Networks Supplement}
\def\opteng{Optical Engineering}
\def\optlett{Optics Letters}
\def\opres{Operations Research}
\def\percep{Perception (London)}
\def\PTRSL{Philosophical Transactions of the Royal Society of
                    London B}
\def\physica{Physica}
\def\pra{Physical Review A}
\def\prb{Physical Review B}
\def\prl{Physical Review Letters}
\def\pscrip{Physica Scripta}
\def\plett{Physics Letters}
\def\PNAS{Proceedings of the National Academy of Sciences, USA}
\def\PRSLB{Proceedings of the Royal Society of London B}
\def\psyrev{Psychological Review}
\def\qrb{Quarterly Reviews of Biophysics}
\def\rmp{Reviews of Modern Physics}
\def\sciam{Scientific American}
\def\science{Science}
\def\SAML{Skrifter for Anvendt Matematik og Lingvistik}
\def\tprobapp{Theory of Probability and Its Applications}
\def\zpb{Zeitschrift f\"ur Physik B}

% ---------------------- Conference Proceedings etc -----------------------
% (Some others are in the individual references)

% Les Houches 1986 (Feb-Mar 1985)
\def\leshouches#1{\inbook{Disordered Systems and Biological
   Organization}{Les Houches 1985}{\eds E. Bienenstock,
   F. Fogelman-Souli\'e, and G. Weisbuch}{}{#1}{Berlin: Springer-Verlag}}

% Snowbird 1986 (Apr 1986)
\def\snowbird#1{\inbook{Neural Networks for Computing}
   {Snowbird 1986}
   {\ed J.S. Denker}{}{#1}{New York: American Institute of Physics}}

% Heidelberg 1987 (June 1986)
\def\heidelberg#1{\inbook{Heidelberg Colloquium on Glassy
   Dynamics}{Heidelberg 1986}{\eds J.L. van Hemmen and
   I. Morgenstern}{}{#1}{Berlin: Springer-Verlag}}

% IEEE 1987 (San Diego, June 1987)
\def\sandiegoI#1#2{\inbook{IEEE First International Conference on
   Neural Networks}{San Diego 1987}{\eds M. Caudill and
   C. Butler}{vol.\ #1}{#2}{New York: IEEE}}

% NIPS 1988 (Denver 1987)
\def\denverI#1{\inbook{Neural Information Processing Systems}
   {Denver 1987}{\ed D.Z. Anderson}{}{#1}{New York: American Institute of
   Physics}}

% Standford VLSI 1987
\def\stanford#1{\inbook{Advanced Research in VLSI: Proceedings of
   the 1987 Stanford Conference}{}{\ed P.
   Losleben}{}{#1}{Cambridge: MIT Press}}

% IEEE 1988 (San Diego, June 1988)
\def\sandiegoII#1#2{\inbook{IEEE International Conference on Neural
   Networks}{San Diego 1988}{}{vol.\ #1}{#2}{New York:
   IEEE}}

% NATO ARW F41 1988
\def\nato#1{\inbook{Neural Computers}{Neuss 1987}
   {\eds R. Eckmiller and Ch. von der Malsburg}{}{#1}{Berlin:
   Springer-Verlag}}

% Paris nEuro'88 1989 (June 1988)
\def\neuro#1{\inbook{Neural Networks from Models to Applications}
   {Paris 1988}{\eds L. Personnaz and G. Dreyfus}{}{#1}{Paris: I.D.S.E.T}}

% Connectionist Summer School 1989 (1988)
\def\sumschool#1{\inbook{Proceedings of the 1988 Connectionist Models
   Summer School}{Pittsburg 1988}{\eds D. Touretzky, G. Hinton, and T.
   Sejnowski}{}{#1}{San Mateo: Morgan Kaufmann}}

% Denver 1989 (Denver 1988)
\def\denverII#1{\inbook{Advances in Neural Information Processing
   Systems I}{Denver 1988}{\ed D.S. Touretzky}{}{#1}{San Mateo: Morgan
   Kaufmann}}

% IEEE proc. 1989 (Washington)
\def\washingtonI#1#2{\inbook{International Joint Conference on
   Neural Networks}{Washington 1989}{}{vol.\ #1}{#2}{New York: IEEE}}

% Koch & Segev
\def\kochsegev#1{\inbook{Methods in Neuronal Modeling: From Synapses
   to Networks}{}{\eds C. Koch and I. Segev}{}{#1}{Cambridge: MIT Press}}

% NIPS proc. 1990 (Denver, Nov. 1989)
\def\denverIII#1{\inbook{Advances in Neural Information Processing
   Systems II}{Denver 1989}{\ed D.S. Touretzky}{}{#1}{San Mateo: Morgan
   Kaufmann}}

% Washington 1990 (Washington Jan 1990)
\def\washingtonII#1#2{\inbook{International Joint
   Conference on Neural Networks}{Washington 1990}{}{vol.\ #1}{#2}{Hillsdale:
   Erlbaum}}

% D\"{u}sseldorf 1990
\def\eckmiller#1{\inbook{Parallel Processing in Neural Systems and
  Computers}{D\"{u}sseldorf 1990}{\eds R. Eckmiller, G. Hartmann, and
  G. Hauske}{}{#1}{Amsterdam: Elsevier}}

% PDP books
\def\PDPone#1#2{{\sl Parallel Distributed
Processing}, vol.\ 1, chap.\ #1.  {\it See\/} Rumelhart, McClelland,
\etal\ [1986]}
\def\PDPtwo#1#2{{\sl Parallel Distributed
Processing}, vol.\ 2, chap.\ #1.  {\it See\/} McClelland, Rumelhart,
\etal\ [1986]}

% Anderson and Rosenfeld book; \inAR or \partinAR placed at end.
\def\inAR{Reprinted in Anderson and Rosenfeld [1988]. }
\def\partinAR{Partially  reprinted in Anderson and Rosenfeld [1988]. }


% ----------------------------- main entries -------------------------------

\paper
{Abu-Mostafa, Y.S. (1989)}
{The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning}
{\journ{\nc}{1}{312--317}}

\paper
{Abu-Mostafa, Y.S. and D. Psaltis (1987)}
{Optical Neural Computers}
{\journ{\sciam}{256}{March 1987, 88--95}}

\paper
{Ackley, D.H., G.E. Hinton, and T.J. Sejnowski (1985)}
{A Learning Algorithm for Boltzmann Machines}
{\journ{\cogsci}{9}{147--169}}
\inAR

\paper
{Ackley, D.H. and M.S. Littman (1990)}
{Generalization and Scaling in Reinforcement Learning}
{\denverIII{550--557}}

\paper
{Ahalt, S.C., A.K. Krishnamurthy, P. Chen, and D.E. Melton (1990)}
{Competitive Learning Algorithms for Vector Quantization}
{\journ{\nn}{3}{277--290}}

\paper
{Almeida, L.B. (1987)}
{A Learning Rule for Asynchronous Perceptrons with Feedback in a Combinatorial
Environment}
{\sandiegoI{II}{609--618}}

\paper
{Almeida, L.B. (1988)}
{Backpropagation in Perceptrons with Feedback}
{\nato{199--208}}

\paper
{Alspector, J. and R.B. Allen (1987)}
{A Neuromorphic VLSI Learning System}
{\stanford{313--349}}

\paper
{Alspector, J., R.B. Allen, V. Hu, and S. Satyanarayana (1988)}
{Stochastic Learning Networks and Their Electronic Implementation}
{\denverI{9--21}}

\paper
{Amari, S.A. (1977)}
{Dynamics of Pattern Formation in Lateral-Inhibition Type Neural Fields}
{\journ{\biocyb}{27}{77---87}}

\paper
{Amari, S.A. (1980)}
{Topographic Organization of Nerve Fields}
{\journ{\bmbiol}{42}{339--364}}

\paper
{Amit, D. (1988)}
{Neural Networks for Counting Chimes}
{\journ{\PNAS}{85}{2141--2145}}

\book
{Amit, D. (1989)}
{Modelling Brain Function}
{Cambridge: Cambridge University Press}

\paper
{Amit, D., H. Gutfreund, and H. Sompolinsky (1985a)}
{Spin-Glass Models of Neural Networks}
{\journ{\pra}{32}{1007--1018}}

\paper
{Amit, D., H. Gutfreund, and H. Sompolinsky (1985b)}
{Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks}
{\journ{\prl}{55}{1530--1533}}

\paper
{Amit, D., H. Gutfreund, and H. Sompolinsky (1987a)}
{Statistical Mechanics of Neural Networks Near Saturation}
{\journ{\annphys}{173}{30--67}}

\paper
{Amit, D., H. Gutfreund, and H. Sompolinsky (1987b)}
{Information Storage in Neural Networks with Low Levels of Activity}
{\journ{\pra}{35}{2293--2303}}

\paper
{Anderson, C.H. and D.C. Van Essen (1987)}
{Shifter Circuits: A Computational Strategy for Dynamic Aspects of Visual
Processing}
{\journ{\PNAS}{84}{6297--6301}}

\paper
{Anderson, D.Z. (1986)}
{Coherent Optical Eigenstate Memory}
{\journ{\optlett}{11}{56--58}}

\paper
{Anderson, J.A. (1968)}
{A Memory Model Using Spatial Correlation Functions}
{\journ{\kyb}{5}{113--119}}

\paper
{Anderson, J.A. (1970)}
{Two Models for Memory Organization}
{\journ{\mbio}{8}{137--160}}

\paper
{Anderson, J.A. and M.C. Mozer (1981)}
{Categorization and Selective Neurons}
{\inbook{Parallel Models of Associative Memory}{}{\eds G.E. Hinton and
J.A. Anderson}{}{213--236}{Hillsdale: Erlbaum}}

\book
{Anderson, J.A. and E. Rosenfeld, eds.\ (1988)}
{Neurocomputing: Foundations of Research}
{Cambridge: MIT Press}

\paper
{Anderson, S., J.W.L. Merrill, and R. Port (1989)}
{Dynamic Speech Categorization with Recurrent Networks}
{\sumschool{398--406}}

\paper
{Ang\'eniol, B., G. de La Croix Vaubois, and J.-Y. Le Texier (1988)}
{Self-Organ\-izing Feature Maps and the Travelling Salesman Problem}
{\journ{\nn}{1}{289--293}}

\book
{Arbib, M.A. (1987)}
{Brains, Machines, and Mathematics}
{Berlin: Springer-Verlag}

\paper
{Bachmann, C.M., L.N. Cooper, A. Dembo, and O. Zeitouni (1987)}
{A Relaxation Model for Memory with High Storage Density}
{\journ{\PNAS}{84}{7529--7531}}

\paper
{Bachrach, J. (1988)}
{Learning to Represent State}
{Master's Thesis, University of Massachusetts, Amherst}

\paper
{Baldi, P. and K. Hornik (1989)}
{Neural Networks and Principal Component Analysis: Learning from Examples
Without Local Minima}
{\journ{\nn}{2}{53--58}}

\paper
{Barhen, J., S. Gulati, and M. Zak (1989)}
{Neural Learning of Constrained Nonlinear Transformations}
{\journ{\computer}{}{June 1989, 67--76}}

\paper
{Barlow, H.B. (1989)}
{Unsupervised Learning}
{\journ{\nc}{1}{295--311}}

\paper
{Barto, A.G. and P. Anandan (1985)}
{Pattern Recognizing Stochastic Learning Automata}
{\journ{\ieeesmc}{15}{360--375}}

\paper
{Barto, A.G. and M.I. Jordan (1987)}
{Gradient Following Without Back-Propagation in Layered Networks}
{\sandiegoI{II}{629--636}}

\paper
{Barto, A.G., R.S. Sutton, and C.W. Anderson (1983)}
{Neuronlike Adaptive Elements That Can Solve Difficult Learning Control
Problems}
{\journ{\ieeesmc}{13}{834--846}}
\inAR

\paper
{Barto, A.G., R.S. Sutton, and C.J.C.H. Watkins (1991)}
{Learning and Sequential Decision Making}
{\inbook{Learning and Computational Neuroscience}{}{\eds M. Gabriel and J.W.
Moore}{}{}{Cambridge: MIT Press}}

\paper
{Baum, E.B. (1986)}
{Towards Practical ``Neural'' Computation for Combinatorial Optimization
Problems}
{\snowbird{53--58}}

\paper
{Baum, E.B. and D. Haussler (1989)}
{What Size Net Gives Valid Generalization?\noperiod}
{\journ{\nc}{1}{151--160}}

\paper
{Baum, E.B. and F. Wilczek (1988)}
{Supervised Learning of Probability Distributions by Neural Networks}
{\denverI{52--61}}

\paper
{Becker, S. and Y. Le Cun (1989)}
{Improving the Convergence of Back-Prop\-agation Learning with Second Order
Methods}
{\sumschool{29--37}}

\paper
{Bengio, Y., R. Cardin, and R. De Mori (1990)}
{Speaker Independent Speech Recognition with Neural Networks and Speech
Knowledge}
{\denverIII{218--225}}

\paper
{Beurle, R.L. (1956)}
{Properties of a Mass of Cells Capable of Regenerating Pulses}
{\journ{\PTRSL}{240}{55--94}}

\paper
{Bienenstock, E.L., L.N. Cooper, and P.W. Munro (1982)}
{Theory for the Development of Neuron Selectivity: Orientation Specificity
and Binocular Interaction in Visual Cortex}
{\journ{\jneuro}{2}{32--48}}
\inAR

\paper
{Bilbro, G., R. Mann, T.K. Miller, W.E. Snyder, D.E. Van den Bout, and M.
White (1989)}
{Optimization by Mean Field Annealing}
{\denverII{91--98}}

\paper
{Bilbro, G.L. and W. Snyder (1989)}
{Range Image Restoration Using Mean Field Annealing}
{\denverII{594--601}}

\book
{Binder, K. and D.W. Heerman (1988)}
{Monte Carlo Simulation in Statistical Mechanics}
{Berlin: Springer-Verlag}

\paper
{Binder, K. and A.P. Young (1986)}
{Spin Glasses: Experimental Facts, Theoretical Concepts, and Open Questions}
{\journ{\rmp}{58}{801--976}}

\paper
{Blakemore, C. and G.F. Cooper (1970)}
{Development of the Brain Depends on the Visual Environment}
{\journ{\nature}{228}{477--478}}

\paper
{Block, H.D. (1962)}
{The Perceptron: A Model for Brain Functioning}
{\journ{\rmp}{34}{123--135}}
\inAR

\paper
{Blumer, A., A. Ehrenfeucht, D. Haussler, and M. Warmuth (1986)}
{Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis
Dimension}
{\inbook{Proceedings of the Eighteenth Annual ACM Symposium on Theory of
Computing}{Berkeley 1986}{}{}{273--282}{Salem: ACM}}

\paper
{Bohr, H., J. Bohr, S. Brunak, R.M.J. Cotterill, B. Lautrup, L. N\o rskov,
O.H. Olsen, and S.B. Petersen (1988)}
{Protein Secondary Structure and Homology
by Neural Networks: The $\alpha$-Helices in Rhodopsin}
{\journ{\febsl}{241}{223--228}}

\paper
{Bourland, H. and Y. Kamp (1988)}
{Auto-Association by Multilayer Perceptrons and Singular Value Decomposition}
{\journ{\biocyb}{59}{291--294}}

\paper
{Brandt, R.D., Y. Wang, A.J. Laub, and S.K. Mitra (1988)}
{Alternative Networks for Solving the Travelling Salesman Problem and the
List-Matching Problem}
{\sandiegoII{II}{333--340}}

\paper
{Brunak, S. and B. Lautrup (1989)}
{Liniedeling med et Neuralt Netv{\ae}rk}
{\journ{\SAML}{14}{55--74}}

\book
{Brunak, S. and B. Lautrup (1990)}
{Neural Networks: Computers with Intuition}
{Singapore: World Scientific}

\book
{Bryson, A.E. and Y.-C. Ho (1969)}
{Applied Optimal Control}
{New York: Blaisdell}

\paper
{Buhmann, J. and K. Schulten (1987)}
{Noise-Driven Temporal Association in Neural Networks}
{\journ{\eul}{4}{1205--1209}}

\paper
{Buhmann, J. and K. Schulten (1988)}
{Storing Sequences of Biased Patterns in Neural Networks with Stochastic
Dynamics}
{\nato{231--242}}

\paper
{Burr, D.J. (1988)}
{An Improved Elastic Net Method for the Travelling Salesman Problem}
{\sandiegoII{I}{69--76}}

\paper
{Caianiello, E.R. (1961)}
{Outline of a Theory of Thought and Thinking Machines}
{\journ{\jtb}{1}{204--235}}

\paper
{Canning, A. and E. Gardner (1988)}
{Partially Connected Models of Neural Networks}
{\journ{\jpa}{21}{3275--3284}}

\paper
{Carpenter, G.A. and S. Grossberg (1987a)}
{A Massively Parallel Architecture for a Self-Organizing Neural Pattern
Recognition Machine}
{\journ{\cvgip}{37}{54--115}}

\paper
{Carpenter, G.A. and S. Grossberg (1987b)}
{ART2: Self-Organization of Stable Category Recognition Codes for Analog
Input Patterns}
{\journ{\applopt}{26}{4919--4930}}

\paper
{Carpenter, G.A. and S. Grossberg (1988)}
{The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network}
{\journ{\computer}{}{March 1988, 77--88}}

\paper
{Casdagli, M. (1989)}
{Nonlinear Prediction of Chaotic Time Series}
{\journ{\physica}{35D}{335--356}}

\paper
{Cater, J.P. (1987)}
{Successfully Using Peak Learning Rates of 10 (and Greater) in
Back-Propagation Networks with the Heuristic Learning Algorithm}
{\sandiegoI{II}{645--651}}

\book
{Caudill, M. (1989)}
{Neural Networks Primer}
{San Francisco: Miller Freeman}

\paper
{Chauvin, Y. (1989)}
{A Back-Propagation Algorithm with Optimal Use of Hidden Units}
{\denverII{519--526}}

\paper
{Cleeremans, A., D. Servan-Schreiber, and J.L. McClelland (1989)}
{Finite State Automata and Simple Recurrent Networks}
{\journ{\nc}{1}{372--381}}

\paper
{Cohen, M.A. and S. Grossberg (1983)}
{Absolute Stability of Global Pattern Formation and Parallel Memory Storage by
Competitive Neural Networks}
{\journ{\ieeesmc}{13}{815--826}}

\paper
{Cohen, M.S. (1986)}
{Design of a New Medium for Volume Holographic Information Processing}
{\journ{\applopt}{25}{2228--2294}}

\paper
{Coolen, A.C.C. and C.C.A.M. Gielen (1988)}
{Delays in Neural Networks}
{\journ{\eul}{7}{281--285}}

\paper
{Cooper, L.N. (1973)}
{A Possible Organization of Animal Memory and Learning}
{\inbook{Collective Properties of Physical Systems}{24th
Nobel Symposium}{\eds B. Lundqvist and S. Lundqvist}{}{252--264}
{New York: Academic Press}}
\inAR

\paper
{Cortes, C. and J.A. Hertz (1989)}
{A Network System for Image Segmentation}
{\washingtonI{I}{121--127}}

\paper
{Cortes, C., A. Krogh, and J.A. Hertz (1987)}
{Hierarchical Associative Networks}
{\journ{\jpa}{20}{4449--4455}}

\paper
{Cottrell, G.W., P. Munro, and D. Zipser (1987)}
{Learning Internal Representations from Gray-Scale Images: An Example of
Extensional Programming}
{\inbook{Ninth Annual Conference of the Cognitive Science
Society}{Seattle 1987}{}{}{462--473}{Hillsdale: Erlbaum}}

\paper
{Cottrell, M. and J.C. Fort (1986)}
{A Stochastic Model of Retinotopy: A Self Organizing Process}
{\journ{\biocyb}{53}{405--411}}

\paper
{Cover, T.M. (1965)}
{Geometrical and Statistical Properties of Systems of Linear
Inequalities with Applications in Pattern Recognition}
{\journ{\ieeetec}{14}{326--334}}

\paper
{Cowan, J.D. and D.H. Sharp (1988a)}
{Neural Nets and Artificial Intelligence}
{\journ{\daed}{117}{85--121}}

\paper
{Cowan, J.D. and D.H. Sharp (1988b)}
{Neural Nets}
{\journ{\qrb}{21}{365--427}}

\paper
{Cragg, B.G. and H.N.V. Temperley (1954)}
{The Organization of Neurones: A Cooperative Analogy}
{\journ{\EEGCN}{6}{85--92}}

\paper
{Cragg, B.G. and H.N.V. Temperley (1955)}
{Memory: The Analogy with Ferromagnetic Hysteresis}
{\journ{\brain}{78}{II,304--316}}

\paper
{Crick, F. (1989)}
{The Recent Excitement About Neural Networks}
{\journ{\nature}{337}{129--132}}

\paper
{Crisanti, A. and H. Sompolinsky (1987)}
{Dynamics of Spin Systems with Randomly
Asymmetric Bonds: Langevin Dynamics and a Spherical Model}
{\journ{\pra}{36}{4922--4939}}

\paper
{Crisanti, A., D.J. Amit, and H. Gutfreund (1986)}
{Saturation Level of the Hopfield Model for Neural Network}
{\journ{\eul}{2}{337--341}}

\paper
{Cybenko, G. (1988)}
{Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient}
{Technical Report, Department of Computer Science, Tufts University,
Medford, MA}

\paper
{Cybenko, G. (1989)}
{Approximation by Superpositions of a Sigmoidal Function}
{\journ{\mcss}{2}{303--314}}

\paper
{Dehaene, S., J.-P. Changeux, and J.-P. Nadal (1987)}
{Neural Networks That Learn Temporal Sequences by Selection}
{\journ{\PNAS}{84}{2727--2731}}

\paper
{Denker, J. (1986)}
{Neural Network Refinements and Extensions}
{\snowbird{121--128}}

\paper
{Denker, J., D. Schwartz, B. Wittner, S. Solla, R. Howard,
L. Jackel, and J. Hopfield (1987)}
{Large Automatic Learning, Rule Extraction, and Generalization}
{\journ{\cs}{1}{877--922}}

\paper
{Derrida, B., E. Gardner, and A. Zippelius (1987)}
{An Exactly Soluble Asymmetric Neural Network Model}
{\journ{\eul}{4}{167--173}}

\paper
{Derthick, M. (1984)}
{Variations on the Boltzmann Machine}
{Technical Report CMU--CS--84--120, Department of
Computer Science, Carnegie Mellon University, Pittsburgh, PA}

\paper
{DeSieno, D. (1988)}
{Adding a Conscience to Competitive Learning}
{\sandiegoII{I}{117--124}}

\book
{Devijver, P.A. and J. Kittler (1982)}
{Pattern Recognition: A Statistical Approach}
{London: Prentice-Hall}

\paper
{Diederich, S. and M. Opper (1987)}
{Learning of Correlated Patterns in Spin-Glass Networks by Local Learning
Rules}
{\journ{\prl}{58}{949--952}}

\book
{Duda, R.O. and P.E. Hart (1973)}
{Pattern Classification and Scene Analysis}
{New York: Wiley}

\paper
{Durbin, R. and D. Willshaw (1987)}
{An Analogue Approach to the Travelling
Salesman Problem Using an Elastic Net Method}
{\journ{\nature}{326}{689--691}}

\paper
{Elman, J.L. (1990)}
{Finding Structure in Time}
{\journ{\cogsci}{14}{179--211}}

\paper
{Elman, J.L. and D. Zipser (1988)}
{Learning the Hidden Structure of Speech}
{\journ{\jasa}{83}{1615--1626}}

\paper
{Fahlman, S.E. (1989)}
{Fast-Learning Variations on Back-Propagation: An Empirical Study}
{\sumschool{38--51}}

\paper
{Fahlman, S.E. and C. Lebiere (1990)}
{The Cascade-Correlation Learning Architecture}
{\denverIII{524--532}}

\paper
{Farhat, N.H. (1987)}
{Optoelectronic Analogs of Self-Programming Neural Nets: Architectures and
Methods for Implementing Fast Stochastic Learning by Simulated Annealing}
{\journ{\applopt}{26}{5093--5103}}

\paper
{Farhat, N.H., D. Psaltis, A. Prata, and E. Paek (1985)}
{Optical Implementation of the Hopfield Model}
{\journ{\applopt}{24}{1469--1475}}
\inAR

\paper
{Farmer, D. and J. Sidorowich (1987)}
{Predicting Chaotic Time Series}
{\journ{\prl}{59}{845--848}}

\paper
{Farmer, D. and J. Sidorowich (1988)}
{Exploiting Chaos to Predict the Future and Reduce Noise}
{\inbook{Evolution, Learning, and Cognition}{}{\ed W.C. Lee}{}{277--330}
{Singapore: World Scientific}}

\paper
{Feldman, J.A. and D.H. Ballard (1982)}
{Connectionist Models and Their Properties}
{\journ{\cogsci}{6}{205--254}}
\inAR

\book
{Feller, W. (1968)}
{An Introduction to Probability Theory and Its Applications{\rm , vol.\ I}}
{New York: Wiley}

\book
{Fischer, K.H. and J.A. Hertz (1990)}
{Spin Glasses}
{Cambridge: Cambridge University Press}

\paper
{F\"oldi\'ak, P. (1989)}
{Adaptive Network for Optimal Linear Feature Extraction}
{\washingtonI{I}{401--405}}

\paper
{Franzini, M.A. (1987)}
{Speech Recognition with Back Propagation}
{\inbook{Proceedings of the Ninth Annual Conference of the IEEE Engineering in
Medicine and Biology Society}{Boston 1987}{}{}{1702--1703}{New York: IEEE}}

\paper
{Frean, M (1990)}
{The Upstart Algorithm: A Method for Constructing and Training Feedforward
Neural Networks}
{\journ{\nc}{2}{198--209}}

\paper
{Fu, Y. and P.W. Anderson (1986)}
{Application of Statistical Mechanics to
NP-Complete Problems in Combinatorial Optimization}
{\journ{\jpa}{19}{1605--1620}}

\paper
{Fukushima, K. (1975)}
{Cognitron: A Self-Organizing Multilayered Neural Network}
{\journ{\biocyb}{20}{121--136}}

\paper
{Fukushima, K. (1980)}
{Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of
Pattern Recognition Unaffected by Shift in Position}
{\journ{\biocyb}{36}{193--202}}

\paper
{Fukushima, K., S. Miyake, and T. Ito (1983)}
{Neocognitron: A Neural Network Model for a Mechanism of Visual
Pattern Recognition}
{\journ{\ieeesmc}{13}{826--834}}
\inAR

\paper
{Gallant, S.I. (1986)}
{Optimal Linear Discriminants}
{\inbook{Eighth International Conference on Pattern
Recognition}{Paris 1986}{}{}{849--852}{New York: IEEE}}

\paper
{Gardner, E. (1987)}
{Maximum Storage Capacity in Neural Networks}
{\journ{\eul}{4}{481--485}}

\paper
{Gardner, E. (1988)}
{The Space of Interactions in Neural Network Models}
{\journ{\jpa}{21}{257--270}}

\paper
{Gardner, E. and B. Derrida (1988)}
{Optimal Storage Properties of Neural Network Models}
{\journ{\jpa}{21}{271--284}}

\paper
{Gardner, E. and B. Derrida (1989)}
{Three Unfinished Works on the Optimal Storage Capacity of Networks}
{\journ{\jpa}{22}{1983--1994}}

\paper
{Gardner, E., H. Gutfreund, and I. Yekutieli (1989)}
{The Phase Space of Interactions in Neural Networks with Definite Symmetry}
{\journ{\jpa}{22}{1995--2008}}

\book
{Garey, M.R. and D.S. Johnson (1979)}
{Computers and Intractability: A Guide to the Theory of NP-Completeness}
{New York: Freeman}

\paper
{Geman, S and D. Geman (1984)}
{Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration
of Images}
{\journ{\ieeetpami}{6}{721--741}}
\inAR

\book
{Geszti, T. (1990)}
{Physical Models of Neural Networks}
{Singapore: World Scientific}

\paper
{Geszti, T. and F. P\'azm\'andi (1987)}
{Learning Within Bounds and Dream Sleep}
{\journ{\jpa}{20}{L1299--L1303}}

\paper
{Glauber, R.J. (1963)}
{Time-Dependent Statistics of the Ising Model}
{\journ{\jmp}{4}{294--307}}

\book
{Goldberg, D.E. (1989)}
{Genetic Algorithms in Search, Optimization, and Machine Learning}
{Reading: Addison-Wesley}

\paper
{Golomb, D., N. Rubin, and H. Sompolinsky (1990)}
{Willshaw Model: Associative Memory with Sparse Coding and Low Firing Rates}
{\journ{\pra}{41}{1843--1854}}

\paper
{Gorman, R.P. and T.J. Sejnowski (1988a)}
{Analysis of Hidden Units in a Layered Network Trained to Classify Sonar
Targets}
{\journ{\nn}{1}{75--89}}

\paper
{Gorman, R.P. and T.J. Sejnowski (1988b)}
{Learned Classification of Sonar Targets Using a Massively-Parallel Network}
{\journ{\ieeetassp}{36}{1135--1140}}

\paper
{Graf, H.P., L.D. Jackel, R.E. Howard, B. Straughn, J.S. Denker, W. Hubbard,
D.M. Tennant, and D. Schwartz (1986)}
{VLSI Implementation of a Neural Network Memory with Several Hundreds of
Neurons}
{\snowbird{182--187}}

\paper
{Graf, D.H. and W.R. LaLonde (1988)}
{A Neural Controller for Collision-Free Movement of General Robot Manipulators}
{\sandiegoII{I}{77--84}}

\paper
{Gray, R.M. (1984)}
{Vector Quantization}
{\journ{\ieeeassp}{}{April 1984, 4--29}}

\paper
{Grossberg, S. (1967)}
{Nonlinear Difference-Differential Equations in Prediction and Learning Theory}
{\journ{\PNAS}{58}{1329--1334}}

\paper
{Grossberg, S. (1968a)}
{Some Nonlinear Networks Capable of Learning a Spatial Pattern of Arbitrary
Complexity}
{\journ{\PNAS}{59}{368--372}}

\paper
{Grossberg, S. (1968b)}
{Some Physiological and Biochemical Consequences of Psychological Postulates}
{\journ{\PNAS}{60}{758--765}}

\paper
{Grossberg, S. (1969)}
{Embedding Fields: A Theory of Learning with Physiological Implications}
{\journ{\jmpsych}{6}{209--239}}

\paper
{Grossberg, S. (1972)}
{Neural Expectation: Cerebellar and Retinal Analogs of Cells Fired by
Learnable or Unlearned Pattern Classes}
{\journ{\kyb}{10}{49--57}}

\paper
{Grossberg, S. (1976a)}
{Adaptive Pattern Classification and Universal Recoding: I. Parallel
Development and Coding of Neural Feature Detectors}
{\journ{\biocyb}{23}{121--134}}
\inAR

\paper
{Grossberg, S. (1976b)}
{Adaptive Pattern Classification and Universal Recoding: II. Feedback,
Expectation, Olfaction, Illusions}
{\journ{\biocyb}{23}{187--202}}

\paper
{Grossberg, S. (1980)}
{How Does the Brain Build a Cognitive Code?\noperiod}
{\journ{\psyrev}{87}{1--51}}
\inAR

\book
{Grossberg, S. (1987a)}
{The Adaptive Brain{\rm , 2 vols}}
{Amsterdam: Elsevier}

\paper
{Grossberg, S. (1987b)}
{Competitive Learning: From Interactive Activation to Adaptive Resonance}
{\journ{\cogsci}{11}{23--63}}

\paper
{Grossman, T. (1990)}
{The CHIR Algorithm for Feed Forward Networks with Binary Weights}
{\denverIII{516--523}}

\paper
{Grossman, T., R. Meir, and E. Domany (1989)}
{Learning by Choice of Internal Representations}
{\journ{\cs}{2}{555--575}}

\paper
{Gutfreund, H. (1988)}
{Neural Networks with Hierarchically Correlated Patterns}
{\journ{\pra}{37}{570--577}}

\paper
{Gutfreund, H. and M. M\'ezard (1988)}
{Processing of Temporal Sequences in Neural Networks}
{\journ{\prl}{61}{235--238}}

\paper
{Gutzmann, K. (1987)}
{Combinatorial Optimization Using a Continuous State Boltzmann Machine}
{\sandiegoI{III}{721--734}}

\paper
{Gy\"orgyi, G. (1990)}
{Inference of a Rule by a Neural Network with Thermal Noise}
{\journ{\prl}{64}{2957--2960}}

\paper
{Gy\"orgyi, G. and N. Tishby (1990)}
{Statistical Theory of Learning a Rule}
{\inbook{Neural Networks and Spin Glasses}{}{\eds W.K. Theumann and
R. Koeberle}{}{}{Singapore: World Scientific}}

\paper
{Hanson, S.J. and L. Pratt (1989)}
{A Comparison of Different Biases for Minimal Network Construction with
Back-Propagation}
{\denverII{177--185}}

\paper
{Harp, S.A., T. Samad, and A. Guha (1990)}
{Designing Application-Specific Neural Networks Using the Genetic Algorithm}
{\denverIII{447--454}}

\paper
{Hartman, E.J., J.D. Keeler, and J.M. Kowalski (1990)}
{Layered Neural Networks with Gaussian Hidden Units As Universal
Approximations}
{\journ{\nc}{2}{210--215}}

\book
{Hebb, D.O. (1949)}
{The Organization of Behavior}
{New York: Wiley}
\partinAR

\paper
{Hecht-Nielsen, R. (1987a)}
{Combinatorial Hypercompression}
{\sandiegoI{II}{455--461}}

\paper
{Hecht-Nielsen, R. (1987b)}
{Counterpropagation Networks}
{\journ{\applopt}{26}{4979--4984}}

\paper
{Hecht-Nielsen, R. (1988)}
{Applications of Counterpropagation Networks}
{\journ{\nn}{1}{131--139}}

\paper
{Hecht-Nielsen, R. (1989)}
{Theory of the Backpropagation Neural Network}
{\washingtonI{I}{593--605}}

\paper
{Hegde, S.U., J.L. Sweet, and W.B. Levy (1988)}
{Determination of Parameters in a Hopfield/Tank Computational Network}
{\sandiegoII{II}{291--298}}

\paper
{van Hemmen, J.L., L.B. Ioffe, R. K\"uhn, and M. Vaas (1990)}
{Increasing the Efficiency of a Neural Network through Unlearning}
{\journ{\physica}{163A}{386--392}}

\paper
{van Hemmen, J.L. and R. K\"uhn (1986)}
{Nonlinear Neural Networks}
{\journ{\prl}{57}{913--916}}

\paper
{van Hemmen, J.L. and R.G. Palmer (1979)}
{The Replica Method and a Solvable Spin Glass Model}
{\journ{\jpa}{12}{563--580}}

\paper
{Hertz, J.A. (1989)}
{A Gauge Theory in Computational Vision: A Model for Outline Extraction}
{\journ{\pscrip}{39}{161--167}}

\paper
{Hertz, J.A. (1990)}
{Statistical Dynamics of Learning}
{Preprint 90/34 S, Nordita, Copenhagen, Denmark}

\paper
{Hertz, J.A., G. Grinstein, and S. Solla (1986)}
{Memory Networks with Asymmetric Bonds}
{\snowbird{212--218}}

\paper
{Hertz, J.A., G. Grinstein, and S. Solla (1987)}
{Irreversible Spin Glasses and  Neural Networks}
{\heidelberg{538--546}}

\paper
{Hertz, J.A., A. Krogh, and G.I. Thorbergsson (1989)}
{Phase Transitions in Simple Learning}
{\journ{\jpa}{22}{2133--2150}}

\paper
{Herz, A., B. Sulzer, R. K\"uhn, and J.L. van Hemmen (1989)}
{Hebbian Learning Reconsidered: Representation of Static and Dynamic Objects
in Associative Neural Nets}
{\journ{\biocyb}{60}{457--467}}

\paper
{Hinton, G.E. (1986)}
{Learning Distributed Representations of Concepts}
{\inbook{Proceedings of the Eighth Annual Conference of the Cognitive Science
Society}{Amherst 1986}{}{}{1--12}{Hillsdale: Erlbaum}}

\paper
{Hinton, G.E. (1989)}
{Deterministic Boltzmann Learning Performs Steepest Descent in Weight Space}
{\journ{\nc}{1}{143--150}}

\paper
{Hinton, G.E. and T.J. Sejnowski (1983)}
{Optimal Perceptual Inference}
{\inbook{Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition}{Washington 1983}{}{}{448--453}{New York: IEEE}}

\paper
{Hinton, G.E. and T.J. Sejnowski (1986)}
{Learning and Relearning in Boltzmann Machines}
{In \PDPone{7}{282--317}}

\paper
{Hopfield, J.J. (1982)}
{Neural Networks and Physical Systems with Emergent Collective Computational
Abilities}
{\journ{\PNAS}{79}{2554--2558}}
\inAR

\paper
{Hopfield, J.J. (1984)}
{Neurons with Graded Responses
Have Collective Computational Properties Like Those of Two-State Neurons}
{\journ{\PNAS}{81}{3088--3092}}
\inAR

\paper
{Hopfield, J.J. (1987)}
{Learning Algorithms and Probability Distributions in Feed-Forward and
Feed-Back Networks}
{\journ{\PNAS}{84}{8429--8433}}

\paper
{Hopfield, J.J., D.I. Feinstein, and R.G. Palmer (1983)}
{``Unlearning'' Has a Stabilizing Effect in Collective Memories}
{\journ{\nature}{304}{158--159}}

\paper
{Hopfield, J.J. and D.W. Tank (1985)}
{``Neural'' Computation of Decisions in Optimization Problems}
{\journ{\biocyb}{52}{141--152}}

\paper
{Hopfield, J.J. and D.W. Tank (1986)}
{Computing with Neural Circuits: A Model}
{\journ{\science}{233}{625--633}}

\paper
{Hopfield, J.J. and D.W. Tank (1989)}
{Neural Architecture and Biophysics for Sequence Recognition}
{\inbook{Neural Models of Plasticity}{}{\eds J.H. Byrne and
W.O. Berry}{}{363--377}{San Diego: Academic Press}}

\paper
{Hornik, K., M. Stinchcombe, and H. White (1989)}
{Multilayer Feedforward Networks Are Universal Approximators}
{\journ{\nn}{2}{359--366}}

\paper
{Hsu, K, D. Brady, and D. Psaltis (1988)}
{Experimental Demonstration of Optical Neural Computers}
{\denverI{377--386}}

\book
{Huang, K. (1987)}
{Statistical Mechanics}
{New York: Wiley}

\paper
{Huang, W.Y. and R.P. Lippmann (1988)}
{Neural Net and Traditional Classifiers}
{\denverI{387--396}}

\paper
{Hubel, D.H. and T.N. Wiesel (1962)}
{Receptive Fields, Binocular Interaction, and Functional Architecture in
the Cat's Visual Cortex}
{\journ{\jphysiol}{160}{106--154}}

\paper
{Hueter, G.J. (1988)}
{Solution of the Travelling Salesman Problem with an Adaptive Ring}
{\sandiegoII{I}{85--92}}

\paper
{Hush, D.R. and J.M. Salas (1988)}
{Improving the Learning Rate of Back-Propagation with the Gradient Reuse
Algorithm}
{\sandiegoII{I}{441--447}}

\paper
{Jacobs, R.A. (1988)}
{Increased Rates of Convergence Through Learning Rate Adaptation}
{\journ{\nn}{1}{295--307}}

\paper
{Johnson, D.S., C.R. Aragon, L.A. McGeoch, and C. Schevon (1989)}
{Optimization by Simulated Annealing: An Experimental Evaluation; Part I,
Graph Partitioning}
{\journ{\opres}{37}{865--891}.  Parts II and III are
expected to appear in 1990}

\book
{Jolliffe, I.T. (1986)}
{Principal Component Analysis}
{New York: Springer-Verlag}

\paper
{Jordan, M.I. (1986)}
{Attractor Dynamics and Parallelism  in a Connectionist Sequential Machine}
{\inbook{Proceedings of the Eighth Annual Conference of the Cognitive Science
Society}{Amherst 1986}{}{}{531--546}{Hillsdale: Erlbaum}}

\paper
{Jordan, M.I. (1989)}
{Serial Order: A Parallel, Distributed Processing Approach}
{\inbook{Advances in Connectionist Theory: Speech}{}{\eds J.L. Elman and
D.E. Rumelhart}{}{}{Hillsdale: Erlbaum}}

\paper
{Kahng, A.B. (1989)}
{Travelling Salesman Heuristics and Embedding Dimension in the Hopfield Model}
{\washingtonI{I}{513--520}}

\paper
{Kammen, D.M. and A.L. Yuille (1988)}
{Spontaneous Symmetry-Breaking Energy Functions and the Emergence of
Orientation Selective Cortical Cells}
{\journ{\biocyb}{59}{23--31}}

\book
{Kandel, E.R. and J.H. Schwartz (1985)}
{Principles of Neural Science\/ {\rm (2nd ed.)}}
{New York: Elsevier}

\paper
{Kanter, I. and H. Sompolinsky (1987)}
{Associative Recall of Memory Without Errors}
{\journ{\pra}{35}{380--392}}

\paper
{Kerszberg, M. and A. Zippelius (1990)}
{Synchronization in Neural Assemblies}
{\journ{\pscrip}{T33}{54--64}}

\paper
{Kinzel, W. and M. Opper (1990)}
{Dynamics of Learning}
{\inbook{Physics of Neural Networks}{}{\eds E. Domany, J.L. van Hemmen,
and K. Schulten}{vol.\ I}{}{Berlin: Springer-Verlag}}

\paper
{Kirkpatrick, S., C.D. Gelatt Jr., and M.P. Vecchi (1983)}
{Optimization by Simulated Annealing}
{\journ{\science}{220}{671--680}}
\inAR

\paper
{Kirkpatrick, S. and G. Toulouse (1985)}
{Configuration Space Analysis of Travelling Salesman Problems}
{\journ{\jpp}{46}{1277--1292}}

\paper
{Kleinfeld, D. (1986)}
{Sequential State Generation by Model Neural Networks}
{\journ{\PNAS}{83}{9469--9473}}

\paper
{Kleinfeld, D. and H. Sompolinsky (1989)}
{Associative Network Models for Central Pattern Generators}
{\kochsegev{195--246}}

\book
{Klopf, A.H. (1982)}
{The Hedonistic Neuron: A Theory of Memory, Learning, and Intelligence}
{Washington: Hemisphere}

\paper
{Koch, C., J. Luo, C. Mead, and J. Hutchinson (1988)}
{Computing Motion Using Resistive Networks}
{\denverI{422--431}}

\paper
{Koch, C., J. Marroquin, and A. Yuille (1986)}
{Analog ``Neuronal'' Networks in Early Vision}
{\journ{\PNAS}{83}{4263--4267}}

\paper
{Kohonen, T. (1974)}
{An Adaptive Associative Memory Principle}
{\journ{\ieeetc}{C-23}{444--445}}

\paper
{Kohonen, T. (1982)}
{Self-Organized Formation of Topologically Correct Feature Maps}
{\journ{\biocyb}{43}{59--69}}
\inAR

\book
{Kohonen, T. (1989)}
{Self-Organization and Associative Memory\/ {\rm (3rd ed.)}}
{Berlin: Springer-Verlag}

\paper
{Kohonen, T., G. Barna, and R. Chrisley (1988)}
{Statistical Pattern Recognition with Neural Networks: Benchmarking Studies}
{\sandiegoII{I}{61--68}}

\paper
{Kohonen, T., K. M\"akisara, and T. Saram\"aki (1984)}
{Phonotopic Maps---Insightful Representation of Phonological Features for
Speech Recognition}
{\inbook{Proceedings of the Seventh International Conference on Pattern
Recognition}{Montreal 1984}{}{}{182--185}{New York: IEEE}}

\paper
{Kramer, A.H. and A. Sangiovanni-Vincentelli (1989)}
{Efficient Parallel Learning Algorithms for Neural Networks}
{\denverII{40--48}}

\paper
{Krauth, W. and M. M\'ezard (1989)}
{The Cavity Method and the Travelling-Salesman Problem}
{\journ{\eul}{8}{213--218}}

\book
{Krishnaiah, P.R. and L.N. Kanal, eds.\ (1982)}
{Classification, Pattern Recognition, and Reduction of Dimensionality{\rm .
Handbook of Statistics, vol.\ 2}}
{Amsterdam: North Holland}

\paper
{Krogh, A. and J.A. Hertz (1988)}
{Mean Field Analysis of Hierarchical Associative Networks with Magnetization}
{\journ{\jpa}{21}{2211--2224}}

\paper
{Krogh, A. and J.A. Hertz (1990)}
{Hebbian Learning of Principal Components}
{\eckmiller{183--186}}

\paper
{Krogh, A., G.I. Thorbergsson, and J.A. Hertz (1990)}
{A Cost Function for Internal Representations}
{\denverIII{733--740}}

\book
{Kullback, S. (1959)}
{Information Theory and Statistics}
{New York: Wiley}

\book
{Kushner, H.J. and D.S. Clark (1978)}
{Stochastic Approximation Methods for Constrained and Unconstrained Systems}
{New York: Springer-Verlag}

\paper
{Lapedes, A. and R. Farber (1986a)}
{A Self-Optimizing, Nonsymmetrical Neural Net
for Content Addressable Memory and Pattern Recognition}
{\journ{\physica}{22D}{247--259}}

\paper
{Lapedes, A. and R. Farber (1986b)}
{Programming a Massively Parallel, Computation Universal System:
Static Behavior}
{\snowbird{283--298}}

\paper
{Lapedes, A. and R. Farber (1987)}
{Nonlinear Signal Processing Using
Neural Networks: Prediction and System Modelling}
{Technical Report LA--UR--87--2662, Los Alamos National Laboratory,
Los Alamos, NM}

\paper
{Lapedes, A. and R. Farber (1988)}
{How Neural Nets Work}
{\denverI{442--456}}

\book
{Lawler, E.L. (1976)}
{Combinatorial Optimization: Networks and Matroids}
{New York: Holt-Rinehart-Winston}

\book
{Lawler, E.L., J.K. Lenstra, A.H.G. Rinnooy Kan, and D.B. Shmoys, eds.\ (1985)}
{The Travelling Salesman Problem}
{Chichester: Wiley}

\paper
{Le Cun, Y. (1985)}
{Une Proc\'edure d'Apprentissage pour R\'eseau \`a Seuil Assym\'etrique}
{\inbook{Cognitiva 85: A la Fronti\`ere de l'Intelligence Artificielle des
Sciences de la Connaissance des Neurosciences}
{Paris 1985}{}{}{599--604}{Paris: CESTA}}

\paper
{Le Cun, Y., B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and
  L.D. Jackel (1989)}
{Backpropagation Applied to Handwritten Zip Code Recognition}
{\journ{\nc}{1}{541--551}}

\paper
{Le Cun, Y., B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and
  L.D. Jackel (1990)}
{Handwritten Digit Recognition with a Back-Propagation Network}
{\denverIII{396--404}}

\paper
{Le Cun, Y., J.S. Denker, and S.A. Solla (1990)}
{Optimal Brain Damage}
{\denverIII{598--605}}

\paper
{von Lehman, A., E.G. Paek, P.F. Liao, A. Marrakchi, and J.S. Patel (1988)}
{Factors Influencing Learning by Back-Propagation}
{\sandiegoII{I}{335--341}}

\paper
{Liang, F.M. (1983)}
{Ph.D.\ Thesis}
{Stanford University. See Appendix H of {\sl The \TeX
Book}, D.E. Knuth (Reading: Addison Wesley 1986)}

\paper
{Lin, S. and B.W. Kernighan (1973)}
{An Effective Heuristic Algorithm for the Travelling Salesman Problem}
{\journ{\opres}{21}{498--516}}

\paper
{Linsker, R. (1986)}
{From Basic Network Principles to Neural Architecture}
{\journ{\PNAS}{83}{7508--7512, 8390--8394, 8779--8783}}

\paper
{Linsker, R. (1988)}
{Self-Organization in a Perceptual Network}
{\journ{\computer}{}{March 1988, 105--117}}

\paper
{Lippmann, R.P. (1987)}
{An Introduction to Computing with Neural Nets}
{\journ{\ieeeassp}{}{April 1987, 4--22}}

\paper
{Lippmann, R.P. (1989)}
{Review of Neural Networks for Speech Recognition}
{\journ{\nc}{1}{1--38}}

\paper
{Lister, R. (1990)}
{Segment Reversal and the TSP}
{\washingtonII{1}{424--427}}

\paper
{Little, W.A. (1974)}
{The Existence of Persistent States in the Brain}
{\journ{\mbio}{19}{101--120}}

\paper
{Little, W.A. and G.L. Shaw (1975)}
{A Statistical Theory of Short and Long Term Memory}
{\journ{\behbio}{14}{115--133}}
\inAR

\paper
{Little, W.A. and G.L. Shaw (1978)}
{Analytic Study of the Memory Storage Capacity of a Neural Network}
{\journ{\mbio}{39}{281--290}}

\book
{Luenberger, D.G. (1986)}
{Linear and Nonlinear Programming}
{Reading: Addison-Wesley}

\book
{Ma, S.-K. (1985)}
{Statistical Mechanics}
{Philadelphia: World Scientific}

\paper
{MacKay, D.J.C. and K.D. Miller (1990)}
{Analysis of Linsker's Simulation of Hebbian Rules}
{\journ{\nc}{2}{173--187}}

\paper
{Mackey, M.C. and L. Glass (1977)}
{Oscillation and Chaos in Physiological Control Systems}
{\journ{\science}{197}{287}}

\paper
{Makram-Ebeid, S., J.-A. Sirat, and J.-R. Viala (1989)}
{A Rationalized Back-Propagation Learning Algorithm}
{\washingtonI{II}{373--380}}

\paper
{von der Malsburg, Ch. (1973)}
{Self-Organization of Orientation Sensitive Cells in the Striate Cortex}
{\journ{\kyb}{14}{85--100}}
\inAR

\paper
{von der Malsburg, Ch. and J.D. Cowan (1982)}
{Outline of a Theory for the Ontogenesis of Iso-Orientation Domains in Visual
Cortex}
{\journ{\biocyb}{45}{49--56}}

\book
{Mandelbrot, B.B. (1982)}
{The Fractal Geometry of Nature}
{San Francisco: Freeman}

\paper
{Marchand, M., M. Golea, and P. Ruj\'{a}n (1990)}
{A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons}
{\journ{\eul}{11}{487--492}}

\paper
{Marr, D. (1969)}
{A Theory of Cerebellar Cortex}
{\journ{\jphysiol}{202}{437--470}}

\paper
{Marr, D. (1970)}
{A Theory for Cerebral Neocortex}
{\journ{\PRSLB}{176}{161--234}}

\paper
{Marr, D. (1971)}
{Simple Memory: A Theory for Archicortex}
{\journ{\PTRSL}{262}{23--81}}

\book
{Marr, D. (1982)}
{Vision}
{San Francisco: Freeman}
\partinAR

\paper
{Marr, D. and T. Poggio (1976)}
{Cooperative Computation of Stereo Disparity}
{\journ{\science}{194}{283--287}}
\inAR

\paper
{Mattis, D. (1976)}
{Solvable Spin Systems with Random Interactions}
{\journ{\plett}{56A}{421--422}}

\paper
{Mazaika, P.K. (1987)}
{A Mathematical Model of the Boltzmann Machine}
{\sandiegoI{III}{157--163}}

\paper
{McClelland, J.L. and J.L. Elman (1986)}
{Interactive Processes in Speech Perception: The TRACE Model}
{In \PDPtwo{15}{58--121}}

\book
{McClelland, J.L. and D.E. Rumelhart (1988)}
{Explorations in Parallel Distributed Processing: A Handbook of Models,
Programs, and Exercises}
{Cambridge: MIT Press}

\book
{McClelland, J.L., D.E. Rumelhart, and the PDP Research Group (1986)}
{Parallel Distributed Processing: Explorations in the Microstructure
of Cognition, vol.\ 2: Psychological and Biological Models}
{Cambridge: MIT Press}

\paper
{McCulloch, W.S. and W. Pitts (1943)}
{A Logical Calculus of Ideas Immanent in Nervous Activity}
{\journ{\bmbiophys}{5}{115--133}}
\inAR

\paper
{McEliece, R.J., E.C. Posner, E.R. Rodemich, and S.S. Venkatesh (1987)}
{The Capacity of the Hopfield Associative Memory}
{\journ{\ieeeit}{33}{461--482}}

\paper
{McInerny, J.M., K.G. Haines, S. Biafore, and R. Hecht-Nielsen (1989)}
{Back Propagation Error Surfaces Can Have Local Minima}
{\washingtonI{II}{627}}

\book
{Mead, C. (1989)}
{Analog VLSI and Neural Systems}
{Reading: Addison Wesley}

\paper
{Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller
(1953)}
{Equation of State Calculations for Fast Computing Machines}
{\journ{\jcp}{21}{1087--1092}}

\paper
{M\'ezard, M. and J.-P. Nadal (1989)}
{Learning in Feedforward Layered Networks: The Tiling Algorithm}
{\journ{\jpa}{22}{2191--2204}}

\paper
{M\'ezard, M. and G. Parisi (1985)}
{Replicas and Optimization}
{\journ{\jppl}{46}{771--778}}

\paper
{M\'ezard, M. and G. Parisi (1986)}
{A Replica Analysis of the Travelling Salesman Problem}
{\journ{\jpp}{47}{1285--1296}}

\paper
{M\'ezard, M. and G. Parisi (1988)}
{The Euclidean Matching Problem}
{\journ{\jpp}{49}{2019--2025}}

\book
{M\'ezard, M., G. Parisi, and M.A. Virasoro (1987)}
{Spin Glass Theory and Beyond}
{Singapore: World Scientific}

\paper
{Miller, G.F., P.M. Todd, and S.U. Hegde (1989)}
{Designing Neural Networks Using Genetic Algorithms}
{\inbook{Proceedings of the Third International Conference on Genetic
Algorithms}{Arlington 1989}{\ed J.D. Schaffer}{}{379--384}{San Mateo:
Morgan Kaufmann}}

\book
{Minsky, M.L. (1967)}
{Computation: Finite and Infinite Machines}
{Englewood Cliffs: Prentice-Hall}

\book
{Minsky, M.L. and S.A. Papert (1969)}
{Perceptrons}
{Cambridge: MIT Press}
\partinAR

\paper
{Mitchison, G.J. and R.M. Durbin (1989)}
{Bounds on the Learning Capacity of Some Multi-Layer Networks}
{\journ{\biocyb}{60}{345--356}}

\paper
{Montana, D.J. and L. Davis (1989)}
{Training Feedforward Networks Using Genetic Algorithms}
{\inbook{Eleventh International Joint Conference on Artificial
Intelligence}{Detroit 1989}{\ed N.S. Sridharan}{}{762--767}{San Mateo:
Morgan Kaufmann}}

\paper
{Moody, J. and C. Darken (1988)}
{Learning with Localized Receptive Fields}
{\sumschool{133--143}}

\paper
{Moody, J. and C. Darken (1989)}
{Fast Learning in Networks of Locally-Tuned Processing Units}
{\journ{\nc}{1}{281--294}}

\paper
{Moore, B.  (1988)}
{ART1 and Pattern Clustering}
{\sumschool{174--185}}

\paper
{Mozer, M.C. (1989)}
{A Focused Back-Propagation Algorithm for Temporal Pattern Recognition}
{\journ{\cs}{3}{349--381}}

\paper
{Munro, P. (1987)}
{A Dual Back-Propagation Scheme for Scalar Reward Learning}
{\inbook{The Ninth Annual Conference of the Cognitive Science
Society}{Seattle 1987}{}{}{165--176}{Hillsdale: Erlbaum}}

\paper
{Nadal, J.-P., G. Toulouse, J.-P. Changeux, and S. Dehaene (1986)}
{Networks of Formal Neurons and Memory Palimpsests}
{\journ{\eul}{1}{535--542}}

\book
{Narendra, K. and M.A.L. Thathachar (1989)}
{Learning Automata: An Introduction}
{Englewood Cliffs: Prentice-Hall}

\paper
{Nasrabadi, N.M. and Y. Feng (1988)}
{Vector Quantization of Images Based upon the Kohonen Self-Organizing
Feature Maps}
{\sandiegoII{I}{101--108}}

\paper
{Nasrabadi, N.M. and R.A. King (1988)}
{Image Coding Using Vector Quantization: A Review}
{\journ{\ieeetcomm}{36}{957--971}}

\paper
{Nass, M.M. and L.N. Cooper (1975)}
{A Theory for the Development of Feature Detecting Cells in Visual Cortex}
{\journ{\biocyb}{19}{1--18}}

\paper
{Naylor, J. and K.P. Li (1988)}
{Analysis of a Neural Network Algorithm for Vector Quantization of Speech
Parameters}
{\journ{\nnsupp}{1}{310}}

\paper
{von Neumann, J. (1956)}
{Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable
Components}
{\inbook{Automata Studies}{}{\eds C.E. Shannon and J. McCarthy}{}{43--98}
{Princeton: Princeton University Press}}

\paper
{Niranjan, M. and F. Fallside (1990)}
{Neural Networks and Radial Basis Functions in Classifying Static Speech
Patterns}
{\journ{\cspla}{4}{275--289}}

\paper
{Nishimori, H., T. Nakamura, and M. Shiino (1990)}
{Retrieval of Spatio-Temporal Sequence in Asynchronous Neural Network}
{\journ{\pra}{41}{3346--3354}}

\paper
{Nowlan, S.J. (1988)}
{Gain Variation in Recurrent Error Propagation Networks}
{\journ{\cs}{2}{305--320}}

\paper
{Oja, E. (1982)}
{A Simplified Neuron Model As a Principal Component Analyzer}
{\journ{\jmathb}{15}{267--273}}

\paper
{Oja, E. (1989)}
{Neural Networks, Principal Components, and Subspaces}
{\journ{\ijns}{1}{61--68}}

\paper
{Oja, E. and J. Karhunen (1985)}
{On Stochastic Approximation of the Eigenvectors and Eigenvalues of the
Expectation of a Random Matrix}
{\journ{\jama}{106}{69--84}}

\paper
{Opper, M., W. Kinzel, J. Kleinz, and R. Nehl (1990)}
{On the Ability of the Optimal Perceptron to Generalize}
{\journ{\jpa}{23}{L581--L586}}

\paper
{Orland, H. (1985)}
{Mean-Field Theory for Optimization Problems}
{\journ{\jppl}{46}{763--770}}

\paper
{Owens, A.J. and D.L. Filkin (1989)}
{Efficient Training of the Back Propagation Network by Solving a System of
Stiff Ordinary Differential Equations}
{\washingtonI{II}{381--386}}

\paper
{Packard, N.H., J.P Crutchfield, J.D. Farmer, and R.S. Shaw (1980)}
{Geometry from a Time Series}
{\journ{\prl}{45}{712--716}}

\paper
{Palmer, R.G. (1988)}
{Statistical Mechanics Approaches to Complex Optimization Problems}
{\inbook{The Economy As an Evolving Complex System}{}{\eds P.W. Anderson,
K.J. Arrow, and D. Pines.  SFI Studies in the Sciences of Complexity}
{proc.\ vol.\ 5}{177--193}{Redwood City: Addison-Wesley}}

\paper
{Palmer, R.G. (1989)}
{Neural Nets}
{\inbook{Lectures in the Sciences of Complexity}{}{\ed
D.L. Stein.  SFI Studies in the Sciences of Complexity}
{lect.\ vol.\ 1}{439--461}{Redwood City: Addison-Wesley}}

\book
{Papadimitriou, C.H. and K. Steiglitz (1982)}
{Combinatorial Optimization: Algorithms and Complexity}
{Englewood Cliffs: Prentice-Hall}

\paper
{Parga, N. and M.A. Virasoro (1986)}
{The Ultrametric Organization of Memories in a Neural Network}
{\journ{\jpp}{47}{1857--1864}}

\paper
{Parisi, G. (1986)}
{Asymmetric Neural Networks and the Process of Learning}
{\journ{\jpa}{19}{L675--L680}}

\paper
{Parker, D.B. (1985)}
{Learning Logic}
{Technical Report TR--47,
Center for Computational Research in Economics and Management Science,
Massachusetts Institute of Technology, Cambridge, MA}

\paper
{Parker, D.B. (1987)}
{Optimal Algorithms for Adaptive Networks: Second Order Back Propagation,
Second Order Direct Propagation, and Second Order Hebbian Learning}
{\sandiegoI{II}{593--600}}

\paper
{Parks, M. (1987)}
{Characterization of the Boltzmann Machine Learning Rate}
{\sandiegoI{III}{715--719}}

\paper
{Pearlmutter, B.A. (1989a)}
{Learning State Space Trajectories in Recurrent Neural Networks}
{\washingtonI{II}{365--372}}

\paper
{Pearlmutter, B.A. (1989b)}
{Learning State Space Trajectories in Recurrent Neural Networks}
{\journ{\nc}{1}{263--269}}

\paper
{Pearlmutter, B.A. and G.E. Hinton (1986)}
{G-Maximization: An Unsupervised Learning Procedure for Discovering
Regularities}
{\snowbird{333--338}}

\paper
{Peretto, P. (1984)}
{Collective Properties of Neural Networks: A Statistical Physics Approach}
{\journ{\biocyb}{50}{51--62}}

\paper
{Peretto, P. (1988)}
{On Learning Rules and Memory Storage Abilities of Asymmetrical Neural
Networks}
{\journ{\jpp}{49}{711--726}}

\paper
{Peretto, P. and J.J. Niez (1986)}
{Collective Properties of Neural Networks}
{\leshouches{171--185}}

\paper
{P\'erez, R., L. Glass, and R. Shlaer (1975)}
{Development of Specificity in the Cat Visual Cortex}
{\journ{\jmathb}{1}{275--288}}

\paper
{Personnaz, L., I. Guyon, and G. Dreyfus (1985)}
{Information Storage and Retrieval in Spin-Glass-Like Neural Networks}
{\journ{\jppl}{46}{359--365}}

\paper
{Personnaz, L., I. Guyon, and G. Dreyfus (1986)}
{Collective Computational Properties of Neural Networks: New Learning
Mechanisms}
{\journ{\pra}{34}{4217--4228}}

\paper
{Peterson, C. and J.R. Anderson (1987)}
{A Mean Field Theory Learning Algorithm for Neural Networks}
{\journ{\cs}{1}{995--1019}}

\paper
{Peterson, C., S. Redfield, J.D. Keeler, and E. Hartman (1990)}
{An Optoelectronic Architecture for Multilayer Learning in a Single
Photorefractive Crystal}
{\journ{\nc}{2}{25--34}}

\paper
{Peterson, C. and B. S\"{o}derberg (1989)}
{A New Method for Mapping Optimization Problems onto Neural Networks}
{\journ{\ijns}{1}{3--22}}

\paper
{Pineda, F.J. (1987)}
{Generalization of Back-Propagation to Recurrent Neural Networks}
{\journ{\prl}{59}{2229--2232}}

\paper
{Pineda, F.J. (1988)}
{Dynamics and Architecture for Neural Computation}
{\journ{\jcomp}{4}{216--245}}

\paper
{Pineda, F.J. (1989)}
{Recurrent Back-Propagation and the Dynamical Approach to Adaptive Neural
Computation}
{\journ{\nc}{1}{161--172}}

\paper
{Plaut, D., S. Nowlan, and G. Hinton (1986)}
{Experiments on Learning by Back Propagation}
{Technical Report CMU--CS--86--126, Department of
Computer Science, Carnegie Mellon University, Pittsburgh, PA}

\paper
{Poggio, T. and F. Girosi (1990)}
{Regularization Algorithms for Learning That Are Equivalent to Multilayer
Networks}
{\journ{\science}{247}{978--982}}

\paper
{Poggio, T., V. Torre, and C. Koch (1985)}
{Computational Vision and Regularization Theory}
{\journ{\nature}{317}{314--319}}

\paper
{Pomerleau, D.A. (1989)}
{ALVINN: An Autonomous Land Vehicle in a Neural Network}
{\denverII{305--313}}

\book
{Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling (1986)}
{Numerical Recipes}
{Cambridge: Cambridge University Press}

\paper
{Qian, N. and T.J. Sejnowski (1988a)}
{Predicting the Secondary Structure of Globular Proteins Using Neural
Network Models}
{\journ{\jmolecb}{202}{865--884}}

\paper
{Qian, N. and T.J. Sejnowski (1988b)}
{Learning to Solve Random-Dot Stereograms of Dense Transparent Surfaces with
Recurrent Back-Propagation}
{\sumschool{435--443}}

\book
{Rao, C.R. and S.K. Mitra (1971)}
{Generalized Inverse of Matrices and Its Applications}
{New York: Wiley}

\paper
{Ramanujam, J. and P. Sadayappan (1988)}
{Optimization by Neural Networks}
{\sandiegoII{II}{325--332}}

\book
{Rashevsky, N. (1938)}
{Mathematical Biophysics}
{Chicago: University of Chicago Press}

\paper
{Rescorla, R.A. and A.R. Wagner (1972)}
{A Theory of Pavlovian Conditioning: The Effectiveness of Reinforcement and
Nonreinforcement}
{\inbook{Classical Conditioning II: Current Research and Theory}{}{\eds A.H.
Black and W.F. Prokasy}{}{64--69}{New York: Appleton-Century-Crofts}}

\paper
{Ricotti, L.P., S. Ragazzini, and G. Martinelli (1988)}
{Learning of Word Stress in a Sub-Optimal Second Order Back-Propagation Neural
Network}
{\sandiegoII{I}{355--361}}

\paper
{Riedel, U., R. K\"uhn, and J.L. van Hemmen (1988)}
{Temporal Sequences and Chaos in Neural Nets}
{\journ{\pra}{38}{1105--1108}}

\paper
{Ritter, H. and K. Schulten (1986)}
{On the Stationary State of Kohonen's Self-Organizing Sensory Mapping}
{\journ{\biocyb}{54}{99--106}}

\paper
{Ritter, H. and K. Schulten (1988a)}
{Extending Kohonen's Self-Organizing Mapping Algorithm to Learn Ballistic
Movements}
{\nato{393--406}}

\paper
{Ritter, H. and K. Schulten (1988b)}
{Convergence Properties of Kohonen's Topology Conserving Maps: Fluctuations,
Stability, and Dimension Selection}
{\journ{\biocyb}{60}{59--71}}

\paper
{Ritter, H. and K. Schulten (1988c)}
{Kohonen's Self-Organizing Maps: Exploring Their Computational Capabilities}
{\sandiegoII{I}{109--116}}

\paper
{Robinson, A.J. and F. Fallside (1988)}
{Static and Dynamic Error Propagation Networks with Application to Speech
Coding}
{\denverI{632--641}}

\paper
{Rohwer, R. (1990)}
{The ``Moving Targets'' Training Algorithm}
{\denverIII{558--565}}

\paper
{Rohwer, R. and B. Forrest (1987)}
{Training Time-Dependence in Neural Networks}
{\sandiegoI{II}{701--708}}

\paper
{Romeo, F.I. (1989)}
{Simulated Annealing: Theory and Applications to Layout Problems}
{Ph.D.\ Thesis, University of California at Berkeley Memorandum
UCB/ERL--M89/29}

\book
{Rose, D. and V.G. Dobson, eds.\ (1985)}
{Models of the Visual Cortex}
{Chichester: Wiley}

\book
{Rosenblatt, F. (1962)}
{Principles of Neurodynamics}
{New York: Spartan}

\paper
{Rubner, J. and K. Schulten (1990)}
{Development of Feature Detectors by Self-Organization}
{\journ{\biocyb}{62}{193--199}}

\paper
{Rubner, J. and P. Tavan (1989)}
{A Self-Organizing Network for Principal-Component Analysis}
{\journ{\eul}{10}{693--698}}

\paper
{Rumelhart, D.E., G.E. Hinton, and R.J. Williams (1986a)}
{Learning Representations by Back-Propagating Errors}
{\journ{\nature}{323}{533--536}}
\inAR

\paper
{Rumelhart, D.E., G.E. Hinton, and R.J. Williams (1986b)}
{Learning Internal Representations by Error Propagation}
{In \PDPone{8}{318--362}}
\inAR

\book
{Rumelhart, D.E., J.L. McClelland, and the PDP Research Group (1986)}
{Parallel Distributed Processing: Explorations in the Microstructure
of Cognition, vol.\ 1: Foundations}
{Cambridge: MIT Press}

\paper
{Rumelhart, D.E. and D. Zipser (1985)}
{Feature Discovery by Competitive Learning}
{\journ{\cogsci}{9}{75--112}.  Also reprinted in \PDPone{5}{151--193}}

\paper
{Saad, D. and E. Marom (1990a)}
{Learning by Choice of Internal Representations\discretionary{}{}{}---An
Energy Minimization Approach}
{Preprint, Faculty of Engineering, Tel Aviv University, Ramat-Aviv, Israel}

\paper
{Saad, D. and E. Marom (1990b)}
{Training Feed Forward Nets with Binary Weights via a Modified CHIR Algorithm}
{Preprint, Faculty of Engineering, Tel Aviv University, Ramat-Aviv, Israel}

\paper
{Salamon, P., J.D. Nulton, J. Robinson, J. Petersen, G. Ruppeiner,
and L. Liao (1988)}
{Simulated Annealing with Constant Thermodynamic Speed}
{\journ{\cpc}{49}{423--428}}

\paper
{Sanger, T.D. (1989a)}
{Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural
Network}
{\journ{\nn}{2}{459--473}}

\paper
{Sanger, T.D. (1989b)}
{An Optimality Principle for Unsupervised Learning}
{\denverII{11--19}}

\paper
{Sato, M. (1990)}
{A Real Time Learning Algorithm for Recurrent Analog Neural Networks}
{\journ{\biocyb}{62}{237--241}}

\paper
{Scalettar, R. and A. Zee (1988)}
{Emergence of Grandmother Memory in Feed Forward Networks: Learning with
Noise and Forgetfulness}
{\inbook{Connectionist Models and Their Implications: Readings from Cognitive
Science}{}{\eds D. Waltz and J.A. Feldman}{}{309--332}{Norwood: Ablex}}

\paper
{Schwartz, D.B., V.K. Samalam, S.A. Solla, and J.S. Denker (1990)}
{Exhaustive Learning}
{\journ{\nc}{2}{371--382}}

\paper
{Scofield, C.L. (1988)}
{Learning Internal Representations in the Coulomb Energy Network}
{\sandiegoII{I}{271--276}}

\paper
{Sejnowski, T.J., P.K. Kienker, and G. Hinton (1986)}
{Learning Symmetry Groups with Hidden Units: Beyond the Perceptron}
{\journ{\physica}{22D}{260--275}}

\paper
{Sejnowski, T.J. and C.R. Rosenberg (1987)}
{Parallel Networks that Learn to Pronounce English Text}
{\journ{\cs}{1}{145--168}}

\paper
{Sherrington, D. and S. Kirkpatrick (1975)}
{Solvable Model of a Spin Glass}
{\journ{\prl}{35}{1792--1796}}

\paper
{Shimohara, K., T. Uchiyama, and Y. Tokunaga (1988)}
{Back-Propagation Networks for Event-Driven Temporal Sequence Processing}
{\sandiegoII{I}{665--672}}

\paper
{Sietsma, J. and R.J.F. Dow (1988)}
{Neural Net Pruning---Why and How}
{\sandiegoII{I}{325--333}}

\paper
{Silverman, R.H. and A.S. Noetzel (1988)}
{Time-Sequential Self-Organization of Hierarchical Neural Networks}
{\denverI{709--714}}

\paper
{Simard, P.Y., M.B. Ottaway, and D.H. Ballard (1989)}
{Analysis of Recurrent Backpropagation}
{\sumschool{103--112}}

\paper
{Simic, P.D. (1990)}
{Statistical Mechanics As the Underlying Theory of ``Elastic'' and ``Neural''
Optimizations}
{\journ{\network}{1}{89--103}}

\paper
{Sirat, J.-A. and J.-P. Nadal (1990)}
{Neural Trees: A New Tool for Classification}
{Preprint, Laboratoires d'Electronique Philips, Limeil-Bre\'vannes, France}

\paper
{Sivilotti, M.A., M.A. Mahowald, and C.A. Mead (1987)}
{Real-Time Visual Computations Using Analog CMOS Processing Arrays}
{\stanford{295--312}}
\inAR

\paper
{Smolensky, P. (1986)}
{Information Processing in Dynamical Systems: Foundations of Harmony Theory}
{In \PDPone{6}{194--281}}

\paper
{Soffer, B.H., G.J. Dunning, Y. Owechko, and E. Marom (1986)}
{Associative Holographic Memory with Feedback Using Phase-Conjugate Mirrors}
{\journ{\optlett}{11}{118--120}}

\paper
{Solla, S.A. (1989)}
{Learning and Generalization in Layered Neural Networks: The
Contiguity Problem}
{\neuro{168--177}}

\paper
{Solla, S.A., E. Levin, and M. Fleisher (1988)}
{Accelerated Learning in Layered Neural Networks}
{\journ{\cs}{2}{625--639}}

\paper
{Sompolinsky, H. (1987)}
{The Theory of Neural Networks: The Hebb Rules and Beyond}
{\heidelberg{485--527}}

\paper
{Sompolinsky, H., A. Crisanti, and H.J. Sommers (1988)}
{Chaos in Random Neural Networks}
{\journ{\prl}{61}{259--262}}

\paper
{Sompolinsky, H. and I. Kanter (1986)}
{Temporal Association in Asymmetric Neural Networks}
{\journ{\prl}{57}{2861--2864}}

\paper
{Soukoulis, C.M., K. Levin, and G.S. Grest (1983)}
{Irreversibility and Metastability in Spin-Glasses. I. Ising Model}
{\journ{\prb}{28}{1495--1509}}

\paper
{Specht, D.F. (1990)}
{Probabilistic Neural Networks}
{\journ{\nn}{3}{109--118}}

\paper
{Steinbuch, K. (1961)}
{Die Lernmatrix}
{\journ{\kyb}{1}{36--45}}

\paper
{Stokbro, K., D.K. Umberger, and J.A. Hertz (1990)}
{Exploiting Neurons with Localized Receptive Fields to Learn Chaos}
{Preprint 90/28 S, Nordita, Copenhagen, Denmark}

\paper
{Stornetta, W.S.,  T. Hogg, and B.A. Huberman (1988)}
{A Dynamical Approach to Temporal Pattern Processing}
{\denverI{750--759}}

\paper
{Sutton, R.S. (1984)}
{Temporal Credit Assignment in Reinforcement Learning}
{Ph.\ D. Thesis, University of Massachusetts, Amherst}

\paper
{Sutton, R.S. (1988)}
{Learning to Predict by the Methods of Temporal Differences}
{\journ{\mlearn}{3}{9--44}}

\paper
{Sutton, R.S. and A.G. Barto (1991)}
{Time Derivative Models of Pavlovian Reinforcement}
{\inbook{Learning and Computational Neuroscience}{}{\eds M. Gabriel and J.W.
Moore}{}{}{Cambridge: MIT Press}}

\paper
{Szu, H. (1986)}
{Fast Simulated Annealing}
{\snowbird{420--425}}

\paper
{Takens, F. (1981)}
{Detecting Strange Attractors In Turbulence}
{\inbook{Dynamical Systems and Turbulence{\rm .
Lecture Notes in Mathematics, vol.\ 898}}{Warwick 1980}{\eds D.A.
Rand and L.-S. Young}{}{366--381}{Berlin: Springer-Verlag}}

\paper
{Takeuchi, A. and S. Amari (1979)}
{Formation of Topographic Maps and Columnar Microstructures in Nerve Fields}
{\journ{\biocyb}{35}{63--72}}

\paper
{Tank, D.W. and J.J. Hopfield (1986)}
{Simple ``Neural'' Optimization Networks: An A/D Converter, Signal Decision
Circuit, and a Linear Programming Circuit}
{\journ{\ieeetcas}{33}{533--541}}

\paper
{Tank, D.W. and J.J. Hopfield (1987a)}
{Neural Computation by Concentrating Information in Time}
{\journ{\PNAS}{84}{1896--1900}}

\paper
{Tank, D.W. and J.J. Hopfield (1987b)}
{Concentrating Information in Time: Analog Neural Networks with Applications
to Speech Recognition Problems}
{\sandiegoI{IV}{455--468}}

\paper
{Taylor, W.K. (1956)}
{Electrical Simulation of Some Nervous System Functional Activities}
{\inbook{Information Theory}{London 1985}{\ed C. Cherry}{}{314--328}
{London: Butterworths}}

\paper
{Tesauro, G. (1986)}
{Simple Neural Models of Classical Conditioning}
{\journ{\biocyb}{55}{187--200}}

\paper
{Tesauro, G. (1990)}
{Neurogammon Wins Computer Olympiad}
{\journ{\nc}{1}{321--323}}

\paper
{Tesauro, G. and B. Janssens (1988)}
{Scaling Relationships in Back-Propagation Learning}
{\journ{\cs}{2}{39--44}}

\paper
{Tesauro, G. and T.J. Sejnowski (1988)}
{A ``Neural'' Network That Learns to Play Backgammon}
{\denverI{442--456}}

\paper
{Thakoor, A.P., A. Moopenn, J. Lambe, and S.K. Khanna (1987)}
{Electronic Hardware Implementations of Neural Networks}
{\journ{\applopt}{26}{5085--5092}}

\paper
{Ticknor, A.J. and H. Barrett (1987)}
{Optical Implementations of Boltzmann Machines}
{\journ{\opteng}{26}{16--21}}

\paper
{Tishby, N., E. Levin, and S.A. Solla (1989)}
{Consistent Inference of Probabilities in Layered Networks: Predictions
and Generalization}
{\washingtonI{II}{403--410}}

\paper
{Toulouse, G., S. Dehaene, and J.-P. Changeux (1986)}
{Spin Glass Model of Learning by Selection}
{\journ{\PNAS}{83}{1695--1698}}

\paper
{Touretzky, D.S. and D.A. Pomerleau (1989)}
{What's Hidden in the Hidden Layers?\noperiod}
{\journ{\BYTE}{}{August 1989, 227--233}}

\paper
{Tsodyks, M.V. and M.V. Feigel'man (1988)}
{The Enhanced Storage Capacity in Neural Networks with Low Activity Level}
{\journ{\eul}{6}{101--105}}

\paper
{Van den Bout, D.E. and T.K. Miller (1988)}
{A Travelling Salesman Objective Function That Works}
{\sandiegoII{II}{299--303}}

\paper
{Van den Bout, D.E. and T.K. Miller (1989)}
{Improving the Performance of the Hopfield-Tank Neural Network Through
Normalization and Annealing}
{\journ{\biocyb}{62}{129--139}}

\book
{Vapnik, V.N. (1982)}
{Estimation of Dependences Based on Empirical Data}
{Berlin: Springer-Verlag}

\paper
{Vapnik, V.N. and A.Y. Chervonenkis (1971)}
{On the Uniform Convergence of Relative Frequencies of Events to Their
Probabilities}
{\journ{\tprobapp}{16}{264--280}}

\paper
{Vogl, T.P., J.K. Mangis, A.K. Rigler, W.T. Zink, and D.L. Alkon (1988)}
{Accelerating the Convergence of the Back-Propagation Method}
{\journ{\biocyb}{59}{257--263}}

\paper
{Wagner, K. and D. Psaltis (1987)}
{Multilayer Optical Learning Networks}
{\journ{\applopt}{26}{5061--5076}}

\paper
{Waibel, A. (1989)}
{Modular Construction of  Time-Delay Neural Networks for Speech Recognition}
{\journ{\nc}{1}{39--46}}

\paper
{Waibel, A., T. Hanazawa, G. Hinton, K. Shikano, and K. Lang (1989)}
{Phoneme Recognition Using Time-Delay Neural Networks}
{\journ{\ieeetassp}{37}{328--339}}

\paper
{Watrous, R.L. (1987)}
{Learning Algorithms for Connectionist Networks: Applied Gradient Methods of
Nonlinear Optimization}
{\sandiegoI{II}{619--627}}

\book
{Webster (1988)}
{Webster's Ninth New Collegiate Dictionary}
{Springfield: Merriam-Webster}

\paper
{Weisbuch, G. and F. Fogelman-Souli\'e (1985)}
{Scaling Laws for the Attractors of Hopfield Networks}
{\journ{\jppl}{46}{623--630}}

\paper
{Werbos, P. (1974)}
{Beyond Regression: New Tools for Prediction and Analysis in the
Behavioral Sciences}
{Ph.D.\ Thesis, Harvard University}

\paper
{Werbos, P.J. (1987)}
{Building and Understanding Adaptive Systems: A Statistical/Numerical
Approach to Factory Automation and Brain Research}
{\journ{\ieeesmc}{17}{7--20}}

\paper
{Werbos, P.J. (1988)}
{Generalization of Backpropagation with Application to a Recurrent Gas Market
Model}
{\journ{\nn}{1}{339--356}}

\paper
{Whitley, D. and T. Hanson (1989)}
{Optimizing Neural Networks Using Faster, More Accurate Genetic Search}
{\inbook{Proceedings of the Third International Conference on Genetic
Algorithms}{Arlington 1989}{\ed J.D. Schaffer}{}{391--396}{San Mateo:
Morgan Kaufmann}}

\paper
{Widrow, B. (1962)}
{Generalization and Information Storage in Networks of Adaline ``Neurons''}
{\inbook{Self-Organizing Systems 1962}{Chicago 1962}{\eds M.C.
Yovits, G.T. Jacobi, and G.D. Goldstein}{}{435--461}{Washington: Spartan}}

\paper
{Widrow, B., N.K. Gupta, and S. Maitra (1973)}
{Punish/Reward: Learning with a Critic in Adaptive Threshold Systems}
{\journ{\ieeesmc}{3}{455--465}}

\paper
{Widrow, B. and M.E. Hoff (1960)}
{Adaptive Switching Circuits}
{\inbook{1960 IRE WESCON Convention Record}{}{}{part 4}{96--104}{New York:
IRE}}
\inAR

\book
{Wiener, N. (1948)}
{Cybernetics, or Control and Communication in the Animal and the Machine}
{New York: Wiley}

\paper
{Williams, R.J. (1987)}
{A Class of Gradient-Estimating Algorithms for Reinforcement Learning in
Neural Networks}
{\sandiegoI{II}{601--608}}

\paper
{Williams, R.J. (1988a)}
{On the Use of Back-Propagation in Associative Reinforcement Learning}
{\sandiegoII{I}{263--270}}

\paper
{Williams, R.J. (1988b)}
{Towards a Theory of Reinforcement-Learning Connectionist Systems}
{Technical Report NU--CCS--88--3, College of Computer Science, Northeastern
University, Boston, MA}

\paper
{Williams, R.J. and J. Peng (1989)}
{Reinforcement Learning Algorithms As Function Optimizers}
{\washingtonI{II}{89--95}}

\paper
{Williams, R.J. and D. Zipser (1989a)}
{A Learning Algorithm for Continually Running Fully Recurrent Neural Networks}
{\journ{\nc}{1}{270--280}}

\paper
{Williams, R.J. and D. Zipser (1989b)}
{Experimental Analysis of the Real-Time Recurrent Learning Algorithm}
{\journ{\connsci}{1}{87--111}}

\paper
{Willshaw, D.J., O.P. Buneman, and H.C. Longuet-Higgins (1969)}
{Non-Holographic Associative Memory}
{\journ{\nature}{222}{960--962}}
\inAR

\paper
{Willshaw, D.J. and C. von der Malsburg (1976)}
{How Patterned Neural Connections Can Be Set Up by Self-Organization}
{\journ{\PRSLB}{194}{431--445}}

\paper
{Wilson, G.V. and G.S. Pawley (1988)}
{On the Stability of the Travelling Salesman Problem Algorithm of Hopfield
and Tank}
{\journ{\biocyb}{58}{63--70}}

\paper
{Wilson, H.R. and J.D. Cowan (1973)}
{A Mathematical Theory of the Functional Dynamics of Cortical and Thalamic
Nervous Tissue}
{\journ{\kyb}{13}{55--80}}

\book
{Winograd, S. and J.D. Cowan (1963)}
{Reliable Computation in the Presence of Noise}
{Cambridge: MIT Press}

\paper
{Winters, J.H. and C. Rose (1989)}
{Minimum Distance Automata in Parallel Networks for Optimum Classification}
{\journ{\nn}{2}{127--132}}

\book
{Wittgenstein, L. (1958)}
{Philosophical Investigations{\rm .  2nd ed., translated by G.E.M.
Anscombe}}
{Oxford: Blackwell}

\paper
{Wittner, B.S. and J.S. Denker (1988)}
{Strategies for Teaching Layered Networks Classification Tasks}
{\denverI{850--859}}

\paper
{Yuille, A.L., D.M. Kammen, and D.S. Cohen (1989)}
{Quadrature and the Development of Orientation Selective Cortical Cells
by Hebb Rules}
{\journ{\biocyb}{61}{183--194}}

\paper
{Zak, M. (1988)}
{Terminal Attractors for Addressable Memory in Neural Networks}
{\journ{\plett}{133A}{18--22}}

\paper
{Zak, M. (1989)}
{Terminal Attractors in Neural Networks}
{\journ{\nn}{2}{259--274}}

\bye

