<p class="MsoNormal"><span style="font-family:Calibri">Attached is a paper that
describes how fragments of information are *read* and *written* during AP
generation. Between neurons, the synaptic activity allows a similar
process of interaction to develop with or without AP initiation.</span></p>
<p><span style="font-family:Calibri"> <b>The AP <i>is not a simple process
of conduction</i> as described in the textbooks</b> , is a complex
process of interaction between charge densities embedded within molecular
structures (e.g. proteins) and the transient developed flow of electrical
charges <a href="http://www.worldscinet.com/jin/10/1004/S0219635211002865.html" target="_blank"><span style="color:windowtext">http://www.worldscinet.com/jin/10/1004/S0219635211002865.html</span></a>
. The entire phenomenon has been previously described in
neuroelectrodynamics, <a href="http://www.booksonline.iospress.nl/Content/View.aspx?piid=14453" target="_blank">http://www.booksonline.iospress.nl/Content/View.aspx?piid=14453</a></span>
</p>
<p><span style="font-family:Calibri"> <a href="http://www.springerlink.com/content/x1l7388475323758/" target="_blank">http://www.springerlink.com/content/x1l7388475323758/</a>
</span></p>
<p>Besides <em><span style="font-family:Calibri">changes</span></em> in <em><span style="font-family:Calibri">neurotransmitter</span></em> concentrations, the
generated electric field allows <b>a continuous integration
of information in the brain which is required in perceptual and
cognitive processes</b>.<span style="font-family:Calibri"><a href="http://neuroelectrodynamics.blogspot.com/p/cognition-and-consciousness.html" target="_blank"><span style="color:windowtext">http://neuroelectrodynamics.blogspot.com/p/cognition-and-consciousness.html</span></a></span></p>
<p><span style="font-family:Calibri"> This simple, non-Turing process of
computation by interaction <i>can be explained to a six year old, no
*higher* mathematics is needed. The NED framework</i> shows<b><i> a
direct path from structured, organized (re-organized) matter within
neurons to</i></b> <b><i> the dynamics of thought.</i></b></span>
Unfortunately, current Turing models can't accurately reproduce different forms
of computation that occur in the brain, which may partially explain the
failure of AI to achieve fast its goals.</p>
<p><span style="font-family:Calibri">The new framework solves an old dispute
Cajal-Golgi - neuron doctrine vs continuous network idea-- simply
requires to understand that <i>neurons are distinct structures which
exploit physical principles of interaction</i>. </span></p>
<p style="text-align:justify"><span style="font-family:Calibri">The proof of
the entire concept is simple. The extracellularly recorded potentials can be
used to estimate changes in charge density that occur inside neurons. </span><span style="font-family:Calibri"><i><b>Why Learning Computer Science? </b></i>---- F</span><span style="font-family:Calibri">ew
electrophysiologists understand the issue, no need to perform complex cell
membrane recordings or intracellular recordings, use the charge movement model)
<a href="http://www.sciencedirect.com/science/article/pii/S0165027005001743" target="_blank">http://www.sciencedirect.com/science/article/pii/S0165027005001743</a>Multiple
monopoles can describe the current source density of a spike <a href="http://www.sciencedirect.com/science/article/pii/S0165027006002263" target="_blank">http://www.sciencedirect.com/science/article/pii/S0165027006002263</a></span> The monopole technique was experimentally
tested <a href="http://cbmspc.eng.uci.edu/PUBLICATIONS/cwlee:11.pdf" target="_blank">http://cbmspc.eng.uci.edu/PUBLICATIONS/cwlee:11.pdf</a>
. In addition this spatial modulation of spikes rules out <i>a precise
spatial localization of neurons </i> presented in <a href="http://www.springerlink.com/content/xtw35012073m217j/" target="_blank">http://www.springerlink.com/content/xtw35012073m217j/</a>
either using monopole or dipole models </p><span style="font-family:Calibri"></span>
<p>Any comments, suggestions or corrections are welcomed. </p>
<p class="MsoNormal" style="text-autospace:none">-------------------------------------------<br>
Dorian Aur <br>
<br>
<a href="mailto:dorianaur@gmail.com">dorianaur@gmail.com</a> <br>
<br>
-------------------------------------------<span class="txt"></span></p>
<br><br><div class="gmail_quote">On Mon, Dec 26, 2011 at 9:00 AM, <span dir="ltr"><<a href="mailto:bmi-request@lists.cse.msu.edu" target="_blank">bmi-request@lists.cse.msu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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Today's Topics:<br>
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1. Re: Discipline computer science and why learning computer<br>
science (Michael Lamport Commons)<br>
<br>
<br>
----------------------------------------------------------------------<br>
<br>
Message: 1<br>
Date: Sun, 25 Dec 2011 11:56:18 -0500<br>
From: Michael Lamport Commons <<a href="mailto:commons@tiac.net" target="_blank">commons@tiac.net</a>><br>
Subject: Re: [Bmi] Discipline computer science and why learning<br>
computer science<br>
To: Juyang Weng <<a href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>><br>
Cc: bmilist <<a href="mailto:bmi@lists.cse.msu.edu" target="_blank">bmi@lists.cse.msu.edu</a>><br>
Message-ID: <<a href="mailto:4EF755B2.6050102@tiac.net" target="_blank">4EF755B2.6050102@tiac.net</a>><br>
Content-Type: text/plain; charset="iso-8859-1"<br>
<br>
I have a patent on stacked neural networks that when trained can solve<br>
many stage of development problems that flat ones cannot.<br>
<br>
My Best,<br>
<br>
Michael Lamport Commons, Ph.D.<br>
Assistant Clinical Professor<br>
<br>
Department of Psychiatry<br>
Beth Israel Deaconess Medical Center<br>
Harvard Medical School<br>
234 Huron Avenue<br>
Cambridge, MA 02138-1328<br>
<br>
Telephone <a href="tel:%28617%29%20497-5270" value="+16174975270" target="_blank">(617) 497-5270</a><br>
Facsimile <a href="tel:%28617%29%20491-5270" value="+16174915270" target="_blank">(617) 491-5270</a><br>
Cellular <a href="tel:%28617%29%20320--0896" value="+16173200896" target="_blank">(617) 320--0896</a><br>
<a href="mailto:Commons@tiac.net" target="_blank">Commons@tiac.net</a><br>
<a href="http://dareassociation.org/" target="_blank">http://dareassociation.org/</a><br>
<br>
<br>
<br>
<br>
On 12/25/2011 8:07 AM, Juyang Weng wrote:<br>
><br>
> Please give your views and suggestions so that we can improve the BMI<br>
> web in its page: Why-Me?<br>
><br>
><br>
> I Am a Computer Scientist<br>
><br>
> The prevailing approaches in Computer Science (CS) and Artificial<br>
> Intelligence (AI) fall into the domain of symbolic processing. Not<br>
> many researchers have sufficient background in connectionist (neural<br>
> network) approaches, which already have over 30 years of history of<br>
> phenomenal growth. If CS researchers have an opportunity to learn<br>
> brain-like signal processing, they will find that their ideas of<br>
> symbolic reasoning (e.g., finite automata, Hidden Markov Models,<br>
> Markov Decision Process, and knowledge-base) are beautifully used by<br>
> the brain, but in a deeper emergent way. For example, Marvin Minsky<br>
> 1991 correctly criticized that artificial neural networks then were<br>
> "scruffy". The same seems not true any more (Weng 2010) --- the brain<br>
> appears to use emergent representations that are fundamentally<br>
> different from symbolic models such as Finite Automata, Hidden Markov<br>
> Models, and Markov Decision Processes. In addition, we should<br>
> reconsider (symbolic) NP-hard or NP-complete problems in light of new<br>
> brain models. Computational understanding of brain-mind would<br>
> drastically change the "landscape" of CS. As another example, the<br>
> brain of a child learns new concepts and a new language that the<br>
> parents have not heard about before the child birth --- a capability<br>
> likely will solve a wide array of AI bottleneck problems.<br>
> Computational understanding of brain-mind could drastically change the<br>
> "landscape" of AI.<br>
><br>
><br>
> Why Learning Computer Science?<br>
><br>
> Many researchers thought that computers are just tools, as the tools<br>
> help them to automate some tasks (e.g., generate plots). This<br>
> narrow-minded view is no longer true. Computer-like symbolic<br>
> manipulation and recombination have inspired many psychologists and AI<br>
> researchers to question the sufficiency of the traditional artificial<br>
> neural networks (e.g., Minsky 1991). However, many neural network<br>
> researchers do not understand or even care about such questions,<br>
> simply disregarding them as "not my problem". The recent establishment<br>
> (weng 2010) that the base network of symbolic AI systems (i.e., FA) is<br>
> a special case of a brain-mind network DN indicates the necessity and<br>
> urgency for all researchers and students in EE, Psychology,<br>
> neuroscience, biology, and mathematics to learn computer science,<br>
> especially the automata theory and computational complexity theory. To<br>
> understand how the brain biology works, one must understand at least<br>
> how an automaton operates on symbols and how symbols are related to<br>
> meanings in computers. No, traditional AI theories are not close to<br>
> what the brain does, but they are necessary for understand how the<br>
> brain network does symbolic AI.<br>
><br>
><br>
> --<br>
> --<br>
> Juyang (John) Weng, Professor<br>
> Department of Computer Science and Engineering<br>
> MSU Cognitive Science Program and MSU Neuroscience Program<br>
> 3115 Engineering Building<br>
> Michigan State University<br>
> East Lansing, MI 48824 USA<br>
> Tel: <a href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a><br>
> Fax: <a href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a><br>
> <a href="mailto:Email%3Aweng@cse.msu.edu" target="_blank">Email:weng@cse.msu.edu</a><br>
> URL:<a href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a><br>
> ----------------------------------------------<br>
><br>
><br>
><br>
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</blockquote></div><br>