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@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/===========================================================================
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: -------------------------------------------------------------------------
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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ftp> cd /pub/u/rao/papers/ ftp> get endstop.ps ftp> get dynmem.ps ftp> get invar.ps ftp> bye