Tech Report: Space-Time Receptive Fields from Natural Images

[Rajesh Rao]

The following technical report on learning space-time receptive fields
from natural images is available on the WWW page:
http://www.cs.rochester.edu/u/rao/ or via anonymous ftp (see
instructions below).

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) 275-5492            
University of Rochester          FAX:  (716) 461-2018
Rochester  NY  14627-0226        WWW:  http://www.cs.rochester.edu/u/rao/

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	  Efficient Encoding of Natural Time Varying Images
	    Produces Oriented Space-Time Receptive Fields

		 Rajesh P.N. Rao and Dana H. Ballard

		  	Technical Report 97.4
    National Resource Laboratory for the Study of Brain and Behavior
	 Department of Computer Science, University of Rochester
			     August 1997

  The receptive fields of neurons in the mammalian primary visual
  cortex are oriented not only in the domain of space, but in most
  cases, also in the domain of space-time.  While the orientation of a
  receptive field in space determines the selectivity of the neuron to
  image structures at a particular orientation, a receptive field's
  orientation in space-time characterizes important additional
  properties such as velocity and direction selectivity. Previous
  studies have focused on explaining the spatial receptive field
  properties of visual neurons by relating them to the statistical
  structure of static natural images. In this report, we examine the
  possibility that the distinctive spatiotemporal properties of visual
  cortical neurons can be understood in terms of a statistically
  efficient strategy for encoding natural time varying images. We
  describe an artificial neural network that attempts to accurately
  reconstruct its spatiotemporal input data while simultaneously
  reducing the statistical dependencies between its outputs. The
  network utilizes spatiotemporally summating neurons and learns
  efficient sparse distributed representations of its spatiotemporal
  input stream by using recurrent lateral inhibition and a simple
  threshold nonlinearity for rectification of neural responses.  When
  exposed to natural time varying images, neurons in a simulated
  network developed localized receptive fields oriented in both space
  and space-time, similar to the receptive fields of neurons in the
  primary visual cortex.

Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/space-time.ps.Z
WWW URL:        http://www.cs.rochester.edu/u/rao/ 

26 pages; 1040K 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 space-time.ps
ftp> bye