2 pdp.cns TR's available

[Randall C. O'Reilly]

The following two (related) TR's are now available for electronic ftp
or by hardcopy.  Instructions follow the abstracts.

>>> NOTE THAT THE FTP SITE IS OUR OWN, NOT NEUROPROSE <<<


	Object Recognition and Sensitive Periods: A Computational 
		     Analysis of Visual Imprinting
			
	  		  Randall C. O'Reilly

			    Mark H. Johnson

		      Technical Report PDP.CNS.93.1

		    (Submitted to Neural Computation)

Abstract:

Evidence from a variety of methods suggests that a localized portion
of the domestic chick brain, the Intermediate and Medial Hyperstriatum
Ventrale (IMHV), is critical for filial imprinting.  Data further
suggest that IMHV is performing the object recognition component of
imprinting, as chicks with IMHV lesions are impaired on other tasks
requiring object recognition.  We present a neural network model of
translation invariant object recognition developed from computational
and neurobiological considerations that incorporates some features of
the known local circuitry of IMHV.  In particular, we propose that the
recurrent excitatory and lateral inhibitory circuitry in the model,
and observed in IMHV, produces hysteresis on the activation state of
the units in the model and the principal excitatory neurons in IMHV.
Hysteresis, when combined with a simple Hebbian covariance learning
mechanism, has been shown in earlier work to produce translation
invariant visual representations.  To test the idea that IMHV might be
implementing this type of object recognition algorithm, we have used a
simple neural network model to simulate a variety of different
empirical phenomena associated with the imprinting process.  These
phenomena include reversibility, sensitive periods, generalization,
and temporal contiguity effects observed in behavioral studies of
chicks.  In addition to supporting the notion that these phenomena,
and imprinting itself, result from the IMHV properties captured in the
simplified model, the simulations also generate several predictions
and clarify apparent contradictions in the behavioral data.

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	The Self-Organization of Spatially Invariant Representations
			
	  		  Randall C. O'Reilly

			  James L. McClelland

		     Technical Report PDP.CNS.92.5

Abstract:

The problem of computing object-based visual representations can be
construed as the development of invariancies to visual dimensions
irrelevant for object identity.  This view, when implemented in a
neural network, suggests a different set of algorithms for computing
object-based visual representations than the ``traditional'' approach
pioneered by Marr, 1981.  A biologically plausible
self-organizing neural network model that develops spatially invariant
representations is presented.  There are four features of the
self-organizing algorithm that contribute to the development of
spatially invariant representations: temporal continuity of
environmental stimuli, hysteresis of the activation state (via
recurrent activation loops and lateral inhibition in an interactive
network), Hebbian learning, and a split pathway between ``what'' and
``where'' representations.  These constraints are tested with a
backprop network, which allows for the evaluation of the individual
contributions of each constraint on the development of spatially
invariant representations.  Subsequently, a complete model embodying a
modified Hebbian learning rule and interactive connectivity is
developed from biological and computational considerations.  The
activational stability and weight function maximization properties of
this interactive network are analyzed using a Lyapunov function
approach.  The model is tested first on the same simple stimuli used
in the backprop simulation, and then with a more complex environment
consisting of right and left diagonal lines.  The results indicate
that the hypothesized constraints, implemented in a Hebbian network,
were capable of producing spatially invariant representations.
Further, evidence for the gradual integration of both featural
complexity and spatial invariance over increasing layers in the
network, thought to be important for real-world applications, was
obtained.  As the approach is generalizable to other dimensions such
as orientation and size, it could provide the basis of a more complete
biologically plausible object recognition system.  Indeed, this work
forms the basis of a recent model of object recognition in the
domestic chick (O'Reilly & Johnson, 1993, TR PDP.CNS.93.1).

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Retrieval information for pdp.cns TRs:

unix> ftp 128.2.248.152                 # hydra.psy.cmu.edu
Name: anonymous
Password: <email address>
ftp> cd pub/pdp.cns
ftp> binary
ftp> get pdp.cns.93.1.ps.Z		# or, and
ftp> get pdp.cns.92.5.ps.Z
ftp> quit
unix> zcat pdp.cns.93.1.ps.Z | lpr 	# or however you print postscript
unix> zcat pdp.cns.92.5.ps.Z | lpr 

For those who do not have FTP access, physical copies can be requested from
Barbara Dorney <bd1q+ at andrew.cmu.edu>.