From: tychay@juliet.caltech.edu (Chay, Terrence Y.)
Subject: Eyes and Mexican Hats (was Re: brain rom and all that... )
News-Software: VAX/VMS VNEWS 1.41    
Organization: California Institute of Technology
Date: Fri, 4 Sep 1992 09:25:00 GMT

I wrote
   the big thing here at caltech seems to be Hopfield/Mead's analog VLSI design
   they have a analog VLSI neural "eye"... the "eye" is neural network oriented 
   and is more useful for study of Hubel & Weisel and its only possible
   application is possibly for AI computers ("jacking in" *sigh*).

Thomas Grant Edwards (tedwards@src.umd.edu) responded....
   I was tangentially involved with artificial retina chips at JHU, trying
   to do very similar things to the Mahowald Retina at CalTech.  

   Basically, the chips model the "mexican hat" style responses found
   in the retina, which causes Mach Bands.  Computer people have been
   doing this processing for a while using convolution filters on images to
   enhance edges.  The chip is useful because it does the filtering in
   real time at high speed (almost instantly).  

------MACH BANDS (in case anyone doesn't know)
  mach bands are when the eye sees that things are darker and lighter near
edges (edge enhancement). imagine this 1-d intensity profile.

  ^   __________                   ________/\
  |             |                           |
  |             |                           |
  |             |                           |
  |             |                           |
  |             |__________                 | _________
 increasing    ^^^^ real intensity          \/
 intensity          profile of edge      perceived profile

  there are other examples of mach bands but this is the only one i remember
(look it up in _Vision_ by David Marr)

--------some questions
  the "eye" i saw did edge detection also.  isn't most of the processing of
edge-detection performed in the primary visual cortex (V1).  I can't see
how RGC's with a mexican hat response can find edges.  is lining up RGC
neurons (proposed by H&W) considered the "best" way of performing edge
detection? does V1 use alternate methods also to enhance edge detection?

  isn't the mexican hat operator (or difference of gaussians DOG) a
biological compromise between two competing ideals:
   1) The sinc function- an attempt to reconstruct the function from discrete
      sampling to the maximum possible spatial frequency via the nyquist
      theorem. (function = intensity of image)
   2) A gaussian- which removes "noise" in a non-biased way... absolutely
      necessary in "edge-detection" because noise will cause numerous spurious
      "zero-crossings" in the second derivative.  (a zero-crossing in the 
      second derivative of an intensity function is an edge)
am i wrong?  are the retinal ganglion cells performing just #1 and the fact
that only the first 2 orders are preserved a result of hardware limitations
in a retina?

  in what ways can eyes we build be better than the eyes we have?  what are
some practical uses of the VLSI eye? 

  HELL, has anyone heard of any practical/commercial implementations of a
neural network?

          -terrychay
                 TYCHAY@CITJULIE.BITNET
                 tychay@juliet.caltech.edu
Newsgroups: alt.cyberpunk.tech