🚀 go-pugleaf

>[Article copied from DejaNews.  Sorry for the delay in responding.]

In article <60p0qr$soj@hertz.isr.umd.edu>, delliott@Glue.umd.edu
(David L. Elliott) wrote:
>In article <342fb9ae.4111800@news.earthlink.net>,
>Louis Savain <savainl@earthlink.net> wrote:
>>In article <60f4dc$d4j@andromeda.rutgers.edu>
>>           ohaneef@andromeda.rutgers.edu "Omar Haneef" writes:
>> S[utton] & B[arto] have indeed done excellent work.  More recently
>>however, themore interesting news are coming from neurophysiology and
>>those in the field of computational neuroscience (has little to do
>>with traditional ANNs) who are attempting to model the newest findings.
>>
>>  There is a recent discovery (little known in AI circles) by Henry
>>Markram that strongly suggests that a "differential
>>Hebbian" (A term I borrowed from Dr. Leslie Smith of the University of
>>Stirling who may have borrowed it from A. H. Klopf) form of learning
>>is taking place at the single neuron level.  In our humble opinion,
>>this is a truly revolutionary finding.  What Markram and his team at
>>the Weizmann institute for Science have found (see January 10, 1997
>>issue of Science) is that the precise order in which a spike arrives
>>at a synapse and a retrograde or antidromic spike is generated by a
>>target neuron, has a dramatic effect on the strengthening or weakening
>>of the synapse.  If the synaptic potential arrives first (within 10
>>millisecond), the synapse is strengthened otherwise it is weakened.
>>Our own model, which is based on causality, predicted as much.  What
>>neurophysiologists (and everyone else for that matter) are in the dark
>>about is the precise input conditions that lead to the firing of a
>>neuron.  There is nothing wishy-washy about them.  The temporal or
>>causal nature of these conditions is extremely important to learning
>>and is directly related to Markram's discovery.  We think we know what
>>these conditions are, as you will all soon be able to judge for
>>yourselves.
>>
>>> But here is a question: if it does
>>> causal relations, it won't be able to do logical form, right? I mean,
>>> if you represent solely temporally distinct events, what do you do 
>>> about representing simultaneous properties?
>>
>>  First of all, let me say that we try to refrain from using the word
>>"representation".  Representation usually connotes "symbol
>>manipulation" and we think that the fundamental underpinnings of
>>intelligence and learning have little to do with symbols.  To answer
>>your question, note that a network is a distributed, parallel system
>>and synchronous events are automatically taken care of by the
>>parallelism.  In our model, simultaneity is handled *laterally* and
>>sequentiality (deep causality) is extracted *longitudinally*.  As far
>>as logic is concerned, it is our firm conviction that it is not a
>>fundamental prerequisite to learning.  It is learned just like
>>everything else.  IOW, logic emerges.
>>
>>> (I have the greatest respect
>>> for Koch and Sejnowski, of course, but I won't take their word -- if it
>>> exists -- for it).
>>
>>  What Sejnowski and Koch have brought to the surface more than anyone
>>else, IMO, is the role that precise signal timing plays in the
>>operation of the brain.
>>
>>Regards,
>>
>>Louis Savain
>>
>>President, Marengo Media, Inc.
>>
>>
>>Here are some relevant sites:
>>
>>http://www.hhmi.org/communic/bulletin/oct95/crack.htm
>>http://sanjuan.cs.wwu.edu/mobus/
>>http://www.cs.stir.ac.uk/~lss/AntiDromic/AntiDromic.html
>>http://www.sciam.com/explorations/020397brain/020397explorations.html
>>http://www.sat.t.u-tokyo.ac.jp/javier/wsc1.html
>>http://www.klab.caltech.edu/
>>http://www.cnl.salk.edu/CNL/
>
>About neural timing-- 
>It has been known for a long time (cf. work on binaural hearing, the
>centerline neurons of the goldfish, and other matters of neural
>timing) that the dendrite is an important computational unit. Using
>Wilfred Rall's model, think of a dendrite as a cascade of RC circuits,
>with each synapse able to modulate the conductance (gain) of its local
>circuit. This gives a nice "bilinear control system" model for neurons
>that is probably consonant with the Volterra series models used by
>mathematical biologists like Marmorelis.

  I am not sure what you mean by "bilinear control system" model.
What would be the advantage of modulating the conductance of local
circuits in the context of causality?  Please explain.

>If an excitatory synapse is distal to an inhibitory synapse, its input
>is effectively blocked while the inhibiting effect is exercised. The
>usual interpretation of learning in this context is that the diameter
>of the dendrite, hence its local conductance, can be made to grow by
>the increase of tubulin proteins.

  Sorry, I am not sure I understand what you mean by "interpretation
of learning" here.  Learning is achieved when specific causal meanings
(associations) are discovered, IMO.  How is that achieved by growth in
local conductance?

> However, some dendrites show no such
>gnarled structure, and one may suppose that in that case, initially
>many synaptic connections are made, and most weeded out in the process
>of learning.

  Indeed, learning at the single neuron level consists of retaining
the causal inputs that are associated with the effects being detected
and weeding out the others.  It's all causes (conditions) and effects,
IMO.

>As to Savain's comments on the irrelevance of symbols and logic in
>modeling the brain: these points are well taken. IMHO we got off on
>the wrong track in 1960, trying to use Shannon Theory to model how we
>think. 

  Thanks for agreeing.  You may be right about Shanon although I
suspect that Boole and DeMorgan may have set the stage for this long
before the advent of Shanon and electronic computers.  Even though I
now see the fallacy of making logic (nonmonotonic or not) a
prerequisite to intelligence and learning, I had not always held this
view.  We (Marengo Media) wasted several years of research as a
result.

Regards,

Louis Savain

President, Marengo Media, Inc.