Path: utzoo!utgpu!news-server.csri.toronto.edu!rutgers!usc!sdd.hp.com!hplabs!hpcc01!hpwrce!kingsley
From: kingsley@hpwrce.HP.COM (Kingsley Morse)
Newsgroups: comp.ai.neural-nets
Subject: Re: Observations on the State of NN theory
Message-ID: <3430013@hpwrce.HP.COM>
Date: 25 Aug 90 20:17:47 GMT
References: <schraudo.651012988@beowulf>
Organization: Ye Olde Salt Mines
Lines: 26

Alan Filipski writes:
>Complex biological (not just nervous) systems use strategies of
>hierarchical and sequential decomposition.

>Our brains, for example, are not randomly connected nets but have
>thousands of recognizable discrete structures, from tiny nuclei to huge
>cortical sheets.  Many of these are repeated and some are found within
>others.

>Hierarchy and repetition seem to correspond to context-free and regular
>languages, respectively.  I don't know much about GA's, but is seems to
>me that a grammatical encoding might be appropriate to get around that
>scale barrier.  A "mutation" might involve not only tweaking some
>weights or connections, but repeating whole structures or putting them
> together in different  ways.

I like the term "scale barrier". By the way, I wrote the GA so it would
randomly duplicate parts of the genetic code, in hopes of "repeating whole
structures or putting them together in different ways". Biological GAs
call it duplication mutation. I may have done it wrong though, because 
the resultingly "stiff" brain was still thwarted by the "scale barrier".

Would it make sense that cortical columns are the repeated and independent
brain structures that are scalable? if so, should we hard code these in our
GAs?
                                                    Kingsley