[comp.ai.neural-nets] temporal domain in vision

dmocsny@uceng.UC.EDU (daniel mocsny) (09/15/88)

In Science News, vol. 134, July 23, 1988, C. Vaughan reports on the
work of B. Richmond of NIMH and L. Optican of the National Eye
Institute on their multiplex filter model for encoding data on
neural spike trains. The article implies that real neurons multiplex
lots of data onto their spike trains, much more than the simple
analog voltage in most neurocomputer models. I have not seen
Richmond and Optican's papers and the Science News article was
sufficiently watered down to be somewhat baffling. Has anyone
seen the details of this work, and might it lead to a method to
significantly increase the processing power of an artificial neural
network?

Dan Mocsny
Internet: dmocsny@uceng.UC.EDU

lag@cseg.uucp (L. Adrian Griffis) (09/22/88)

In article <233@uceng.UC.EDU>, dmocsny@uceng.UC.EDU (daniel mocsny) writes:
> In Science News, vol. 134, July 23, 1988, C. Vaughan reports on the
> work of B. Richmond of NIMH and L. Optican of the National Eye
> Institute on their multiplex filter model for encoding data on
> neural spike trains. The article implies that real neurons multiplex
> lots of data onto their spike trains, much more than the simple
> analog voltage in most neurocomputer models. I have not seen
> Richmond and Optican's papers and the Science News article was
> sufficiently watered down to be somewhat baffling. Has anyone
> seen the details of this work, and might it lead to a method to
> significantly increase the processing power of an artificial neural
> network?

My understanding is that neurons in the eye depart from a number of
general rules that neurons seem to follow elsewhere in the nervous system.
One such departure is that sections of a neuron can fire independent
of other sections.  This allows the eye to behave as though is has a great
many logical neuron without having to use the the space that the same number
of discrete cellular metabolic systems would require.  I'm not an expert
in this field, but this suggests to me that many of the special tricks
that neurons of the eye employ may be attempts to overcome space limitations
rather than to make other processing schemes possible.  Whether or not
this affects the applicability of such tricks to artificial neural networks
is another matter.  After all, artificial neural networks have space 
limitations of their own.

-- 
  UseNet:  lag@cseg                      L. Adrian Griffis
  BITNET:  AG27107@UAFSYSB

jwl@ernie.Berkeley.EDU (James Wilbur Lewis) (09/22/88)

In article <724@cseg.uucp> lag@cseg.uucp (L. Adrian Griffis) writes:
>In article <233@uceng.UC.EDU>, dmocsny@uceng.UC.EDU (daniel mocsny) writes:
>> In Science News, vol. 134, July 23, 1988, C. Vaughan reports on the
>> work of B. Richmond of NIMH and L. Optican of the National Eye
>> Institute on their multiplex filter model for encoding data on
>> neural spike trains. The article implies that real neurons multiplex
>> lots of data onto their spike trains, much more than the simple
>> analog voltage in most neurocomputer models.
>
>My understanding is that neurons in the eye depart from a number of
>general rules that neurons seem to follow elsewhere in the nervous system.

I think Richmond and Optican were studying cortical neurons.  Retinal
neurons encode information mainly by graded potentials, not spike
trains....another significant difference between retinal architecture
and most of the rest of the CNS.

I was somewhat baffled by the Science News article, too.  For example,
it was noted that the information in the spike trains might be a
result of the cable properties of the axons involved, not necessarily
encoding any "real" information, but this possibility was dismissed with a 
few handwaves.

Another disturbing loose end was the lack of discussion about how this
information might be propogated across synapses.  Considering that
it generally takes input from several other neurons to trigger a 
neural firing, and that the integration time necessary would tend
to smear out any such fine-tuned temporal information,  I don't
see how it could be.

It's an interesting result, but I think they may have jumped the
gun with the conclusion they drew from it.

-- Jim Lewis
   U.C. Berkeley

dmocsny@uceng.UC.EDU (daniel mocsny) (09/26/88)

I have received some e-mail on the work of Richmond and Optican, including
a reference to one of their publications and requests for said reference.
My mailer could not reply to one of these requests (hello Toshitera Homma)
so I post it here.

] From edelman@wheaties.ai.mit.edu Fri Sep 16 13:43 EDT 1988
] You may want to read the following:
]	Temporal encoding of two-dimensional patterns by single
]	units in primate inferior temporal cortex,
]		J. Neurophysiology, 57 (1), Jan. 1987
] Shimon Edelman
] Center for Biological Information Processing
] Dept. of Brain and Cognitive Sciences, MIT

Thanks, Shimon.

Dan Mocsny