Tech Report: Visual Cortex as a Hierarchical Predictor

[Rajesh Rao]

The following technical report on a hierarchical predictor model of
the visual cortex and the complex cell phenomenon of "endstopping" is
available for retrieval via ftp.

Comments and suggestions welcome (This message has been cross-posted -
my apologies to those who received it more than once).

-- 
Rajesh Rao                       Internet: rao at cs.rochester.edu
Dept. of Computer Science	 VOX:  (716) 275-2527              
University of Rochester          FAX:  (716) 461-2018
Rochester  NY  14627-0226        WWW:  http://www.cs.rochester.edu/u/rao/

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            The Visual Cortex as a Hierarchical Predictor


		 Rajesh P.N. Rao and Dana H. Ballard

		       Technical Report 96.4
   National Resource Laboratory for the Study of Brain and Behavior
  	Department of Computer Science, University of Rochester
  		          September, 1996


                             Abstract

   A characteristic feature of the mammalian visual cortex is the
reciprocity of connections between cortical areas [1].  While
corticocortical feedforward connections have been well studied, the
computational function of the corresponding feedback projections has
remained relatively unclear.  We have modelled the visual cortex as a
hierarchical predictor wherein feedback projections carry predictions
for lower areas and feedforward projections carry the difference
between the predictions and the actual internal state.  The activities
of model neurons and their synaptic strength are continually adapted
using a hierarchical Kalman filter [2] that minimizes errors in
prediction.  The model generalizes several previously proposed
encoding schemes [3,4,5,6,7,8] and allows functional interpretations
of a number of well-known psychophysical and neurophysiological
phenomena [9]. Here, we present simulation results suggesting that the
classical phenomenon of endstopping [10,11] in cortical neurons may be
viewed as an emergent property of the cortex implementing a
hierarchical Kalman filter-like prediction mechanism for efficient
encoding and recognition.



Retrieval information:

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

20 pages; 302K compressed.



The following related papers are also available via ftp:
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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 press)

                             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 hierarchical network model of visual
recognition that explains these experimental observations by using a
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 learning rule also derived from the
MDL principle.  The resulting prediction/learning scheme can be viewed
as implementing a form of the Expectation-Maximization (EM) algorithm.
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
WWW URL:        ftp://ftp.cs.rochester.edu/pub/u/rao/papers/dynmem.ps.Z

43 pages; 569K compressed.

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        A Class of Stochastic Models for Invariant Recognition, 
			 Motion, and Stereo

                 Rajesh P.N. Rao and Dana H. Ballard

		       Technical Report 96.1

                              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
  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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Anonymous ftp instructions:

>ftp ftp.cs.rochester.edu
Connected to anon.cs.rochester.edu.
220 anon.cs.rochester.edu FTP server (Version wu-2.4(3)) ready.

Name: [type 'anonymous' here]
331 Guest login ok, send your complete e-mail address as password.

Password: [type your e-mail address here]

ftp> cd /pub/u/rao/papers/
ftp> get endstop.ps
ftp> get dynmem.ps
ftp> get invar.ps
ftp> bye