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[rao@cs.rochester.edu]

psyc at pucc.princeton.edu, cogneuro at ptolemy-ethernet.arc.nasa.gov,
cvnet at skivs.ski.org, inns-l%umdd.bitnet at pucc.princeton.edu,
neuronet at tutkie.tut.ac.jp, vision-list at teleosresearch.com  
Subject: Papers available: Dynamic Models of Visual Recognition


The following two papers on dynamic cortical models of visual
recognition are now available for retrieval via ftp.

Comments/suggestions welcome,
-Rajesh Rao
(rao at cs.rochester.edu)
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        A Class of Stochastic Models for Invariant Recognition, 
			 Motion, and Stereo

                 Rajesh P.N. Rao and Dana H. Ballard

		       (Submitted to NIPS*96)

                              Abstract

  We describe a general framework for modeling transformations in the
  image plane using a stochastic generative model. Algorithms that
  resemble the well-known Kalman filter are derived from the MDL
  principle for estimating both the generative weights and the current
  transformation state. The generative model is assumed to be
  implemented in cortical feedback pathways while the feedforward
  pathways implement an approximate inverse model to facilitate the
  estimation of current state.  Using the above framework, we derive
  stochastic models for invariant recognition, motion estimation, and
  stereopsis, and present preliminary simulation results demonstrating
  recognition of objects in the presence of translations, rotations
  and scale changes.

Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/invar.ps.Z
URL:            ftp://ftp.cs.rochester.edu/pub/u/rao/papers/invar.ps.Z

7 pages; 430K compressed.


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Dynamic Model of Visual Recognition Predicts Neural Response Properties 
                        In The Visual Cortex 

                 Rajesh P.N. Rao and Dana H. Ballard

		   (Neural Computation - in review)

                             Abstract

The responses of visual cortical neurons during fixation tasks can be
significantly modulated by stimuli from beyond the classical receptive
field.  Modulatory effects in neural responses have also been recently
reported in a task where a monkey freely views a natural scene. In
this paper, we describe a stochastic network model of visual
recognition that explains these experimental observations by using a
hierarchical form of the extended Kalman filter as given by the
Minimum Description Length (MDL) principle.  The model dynamically
combines input-driven bottom-up signals with expectation-driven
top-down signals to predict current recognition state.  Synaptic
weights in the model are adapted in a Hebbian manner according to a
stochastic learning rule also derived from the MDL principle.  The
architecture of the model posits an active computational role for the
reciprocal connections between adjoining visual cortical areas in
determining neural response properties.  In particular, the model
demonstrates the possible role of feedback from higher cortical areas
in mediating neurophysiological effects due to stimuli from beyond the
classical receptive field.  Simulations of the model are provided that
help explain the experimental observations regarding neural responses
in both free viewing and fixating conditions.


Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/dynmem.ps.Z
URL:            ftp://ftp.cs.rochester.edu/pub/u/rao/papers/dynmem.ps.Z

32 pages; 534K compressed.

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