two papers on temporal information processing by Dong & Atick

[Dawei Dong]

A theory of temporal information processing in neural systems: how the
early visual pathways, such as LGN, temporally modulate the incoming
signals of natural scenes?

The following two papers explore the above subject by 1) measuring the
temporal power spectrum of natural time-varying images to reveal the
underlying statistical regularities, and, 2) based on the measurements,
using information theory to predict the optimal temporal filter which is
shown in quantitative agreements with physiological experiments.

Dawei Dong


1) ftp://venezia.rockefeller.edu/dawei/papers/95-TIME.ps.Z (213K, 19 pages)

             Statistics of natural time-varying images

                 Dawei W. Dong and Joseph J. Atick

               Computational Neuroscience Laboratory
                    The Rockefeller University
                         1230 York Avenue
                     New York, NY 10021-6399

                             Abstract

   Natural  time-varying   images   possess  substantial  spatiotemporal
   correlations.  We measure these correlations ---  or equivalently the
   power spectrum --- for an ensemble of more  than  a thousand segments
   of  motion  pictures,  and  we  find  significant regularities.  More
   precisely, our measurements show  that  the  dependence  of the power
   spectrum on the spatial frequency, $f$, and  temporal frequency, $w$,
   is in general nonseparable and is given  by  $f^{-m-1} F(w/f)$, where
   $F(w/f)$ is a nontrivial function  of  the  ratio  $w/f$.   We give a
   theoretical derivation of this  scaling  behaviour  and  show that it
   emerges from objects  with  a  static  power  spectrum $\sim f^{-m}$,
   appearing at a wide range of depths and moving with a distribution of
   velocities relative to the observer.  We show that  in  the regime of
   relatively  high  temporal  and  low  spatial  frequencies, the power
   spectrum  becomes  independent  of  the   details   of  the  velocity
   distribution and it is  separable  into  the  product  of spatial and
   temporal power spectra with the temporal part given  by the universal
   power-law $\sim w^{-2}$.   Making  some  reasonable assumptions about
   the  form  of  the  velocity  distribution  we  derive an  analytical
   expression  for  the  spatiotemporal  power  spectrum   which  is  in
   excellent agreement with the data for the entire range of spatial and
   temporal frequencies of our measurements.  The results  in this paper
   have direct implications to neural processing  of time-varying images
   in the visual pathway.

   (Accepted for publication in Network: Computation in Neural Systems)


2) ftp://venezia.rockefeller.edu/dawei/papers/95-LGN.ps.Z (279K, 26 pages)

      Temporal decorrelation: a theory of lagged and nonlagged
            responses in the lateral geniculate nucleus

                 Dawei W. Dong and Joseph J. Atick

               Computational Neuroscience Laboratory
                    The Rockefeller University
                         1230 York Avenue
                     New York, NY 10021-6399

                             Abstract

   Natural time-varying images possess significant temporal correlations
   when  sampled  frame   by   frame   by   the  photoreceptors.   These
   correlations persist even after retinal  processing  and hence, under
   natural  activation  conditions,  the  signal  sent  to  the  lateral
   geniculate  nucleus  is  temporally  redundant  or  inefficient.   We
   explore the hypothesis that the LGN is concerned, among other things,
   with improving  efficiency  of  visual  representation through active
   temporal decorrelation of the retinal  signal  much  in  the same way
   that  the  retina  improves  efficiency  by  spatially  decorrelating
   incoming images.  Using some recently measured statistical properties
   of  time-varying  images,  we  predict  the spatio-temporal receptive
   fields that achieve this decorrelation.  It is shown that, because of
   neuronal nonlinearities, temporal decorrelation requires two response
   types,  the  {\it  lagged}  and  {\it  nonlagged},  just  as  spatial
   decorrelation requires {\it on} and  {\it  off}  response types.  The
   tuning and response properties  of  the  predicted  LGN cells compare
   quantitatively well with  what  is  observed  in recent physiological
   experiments.

   {Network: Computation in Neural Systems}{ Vol~6(2) pp~159-178}