Robustness ?

[rsantiag@nsf.gov]

          "In search of the Engram"

          The problem of robustness from a neurobiological perspective
          seems to originate from work done by Karl Lashley.  He
          sought to find how memory was partitioned in the brain.  He
          thought that memories were kept on certain neuronal circuit
          paths (engrams) and experimented under this hypothesis by
          cutting out parts of brains and seeing if it affected
          memory.  It didn't.  Other work was done by another
          gentlemen named Richard F.  Thompson in the same area.  Both
          speak of the loss of neurons in a system and their theories
          about how integrity was kept.  In particular Karl Lashley
          spoke of memory as holograms.  I think this is what you are
          looking for as far as references.

          As far as every day loss of neurons, well it seems to vary
          from person to person and an actual measure cannot be
          ascertained (this was information was gathered after
          questioning 3 neurobiologists whom all agreed).

          It is more important, with regards to the loss of neurons
          and the question of robustness, to identify the stage in
          which the loss is occuring.  There are three distinct areas
          in neurobiological creation and development that we observe
          this in with any significance.  These are: embryonic
          development, maturation and learning stages, and maturity.
          In embryonic the loss of neurons is rampant but eventually
          leads to the full development of the brain with
          overconnected neurons.  The loss of the neurons are
          important developmentally.  In maturation and learning, the
          loss of neurons helps to define neuronal systems and plays a
          role in their adaption and learning process.  Finally in
          maturity, the loss of neurons is insignificant.  Indeed
          Lashly's model of the holographic mind seems very true.
          The only exception to this is the massive loss of brain
          matter(neurons).  In a situation like this (such as a
          stroke) there can be massive destruction of neuronal
          systems.  In comparison, though, to ANNs it is difficult.
          In ANNs if we are to lose but a few neurons, this could
          represent the loss of 5 to 25 percent of neurons, depending
          on the model.  For a human to lose 5 to 25 of there brains
          could be a devastating proposition.  The question of
          robustness is best reserved for larger systems that would
          suffer the loss of neurons on a more proportianal level to
          current biological NN systems.  It is important though to
          indentify where the loss of neurons fall in your model
          (developing, training, or after you have a stable NN) before
          you attack the problem of robustness. (Most of the previous
          paragraph is derived from "Neurobiology" by Gordon M.
          Sheperd and from miscellaneous sources that he sights in his
          book)

          As for the assumption that ANNs and biological NNs have many
          of the properties, well that is an overwhelmingly boastfull
          statement.  The only similarities that each have is the
          organizational structure to them.  The only experiments with
          ANNs that come close to actual biological neuron modeling is
          a project done by Gary Lynch in California who modelled the
          Olfactory Cortex and some of the NN systems that go into
          smell identification.  He structured each of his neurons to
          function exactly as a bilogical neuron.  His results are
          very fascinating.  Both ANNs and Biological NNs are parallel
          processors but after that, they seperate radically into two
          types of systems.


          Robert A. Santiago
          National Science Foundation
          rsantiag at note.nsf.gov