[Bmi] Why Learning Computer Science? --- Computing by physical interaction in neurons

Tue Jan 24 17:51:13 EST 2012

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.

 *The AP is not a simple process of conduction  as described in the
textbooks* , is a complex process of  interaction between charge densities
embedded within molecular structures (e.g. proteins) and the transient
developed flow of electrical charges
http://www.worldscinet.com/jin/10/1004/S0219635211002865.html  . The entire
phenomenon  has been previously described in neuroelectrodynamics,
http://www.booksonline.iospress.nl/Content/View.aspx?piid=14453

 http://www.springerlink.com/content/x1l7388475323758/

Besides *changes* in *neurotransmitter* concentrations, the generated
electric field    allows *a continuous integration of information in the
brain which is required  in perceptual and  cognitive processes*.
http://neuroelectrodynamics.blogspot.com/p/cognition-and-consciousness.html

 This simple, non-Turing process of computation  by interaction *can be
explained to a six year old, no *higher* mathematics is needed. The NED
framework*  shows* a direct path  from  structured, organized
(re-organized) matter within neurons  to* * the  dynamics of
thought.*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.

The new framework solves an old dispute Cajal-Golgi - neuron doctrine vs
continuous network idea--  simply  requires to understand that *neurons are
distinct structures which exploit  physical principles of interaction*.

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. *Why Learning Computer Science? *---- Few
electrophysiologists understand the issue,  no need to perform complex cell
membrane recordings or intracellular recordings, use the charge movement
model) http://www.sciencedirect.com/science/article/pii/S0165027005001743Multiple
monopoles can describe the current source density  of a spike
http://www.sciencedirect.com/science/article/pii/S0165027006002263 The
monopole technique was experimentally tested
http://cbmspc.eng.uci.edu/PUBLICATIONS/cwlee:11.pdf  . In addition  this
spatial modulation of spikes rules out *a precise spatial localization of
neurons  * presented in
http://www.springerlink.com/content/xtw35012073m217j/ either  using
monopole or dipole  models

Any comments, suggestions or  corrections are welcomed.

-------------------------------------------
Dorian Aur

dorianaur at gmail.com

-------------------------------------------

On Mon, Dec 26, 2011 at 9:00 AM, <bmi-request at lists.cse.msu.edu> wrote:

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>   1. Re: Discipline computer science and why learning computer
>      science (Michael Lamport Commons)
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>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 25 Dec 2011 11:56:18 -0500
> From: Michael Lamport Commons <commons at tiac.net>
> Subject: Re: [Bmi] Discipline computer science and why learning
>        computer        science
> To: Juyang Weng <weng at cse.msu.edu>
> Cc: bmilist <bmi at lists.cse.msu.edu>
> Message-ID: <4EF755B2.6050102 at tiac.net>
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>
> I have a patent on stacked neural networks that when trained can solve
> many stage of development problems that flat ones cannot.
>
> My Best,
>
> Michael Lamport Commons, Ph.D.
> Assistant Clinical Professor
>
> Department of Psychiatry
> Beth Israel Deaconess Medical Center
> Harvard Medical School
> 234 Huron Avenue
> Cambridge, MA 02138-1328
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> Telephone   (617) 497-5270
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> Commons at tiac.net
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>
>
> On 12/25/2011 8:07 AM, Juyang Weng wrote:
> >
> > Please give your views and suggestions so that we can improve the BMI
> > web in its page: Why-Me?
> >
> >
> >         I Am a Computer Scientist
> >
> > The prevailing approaches in Computer Science (CS) and Artificial
> > Intelligence (AI) fall into the domain of symbolic processing. Not
> > many researchers have sufficient background in connectionist (neural
> > network) approaches, which already have over 30 years of history of
> > phenomenal growth. If CS researchers have an opportunity to learn
> > brain-like signal processing, they will find that their ideas of
> > symbolic reasoning (e.g., finite automata, Hidden Markov Models,
> > Markov Decision Process, and knowledge-base) are beautifully used by
> > the brain, but in a deeper emergent way. For example, Marvin Minsky
> > 1991 correctly criticized that artificial neural networks then were
> > "scruffy". The same seems not true any more (Weng 2010) --- the brain
> > appears to use emergent representations that are fundamentally
> > different from symbolic models such as Finite Automata, Hidden Markov
> > Models, and Markov Decision Processes. In addition, we should
> > reconsider (symbolic) NP-hard or NP-complete problems in light of new
> > brain models. Computational understanding of brain-mind would
> > drastically change the "landscape" of CS. As another example, the
> > brain of a child learns new concepts and a new language that the
> > parents have not heard about before the child birth --- a capability
> > likely will solve a wide array of AI bottleneck problems.
> > Computational understanding of brain-mind could drastically change the
> > "landscape" of AI.
> >
> >
> >         Why Learning Computer Science?
> >
> > Many researchers thought that computers are just tools, as the tools
> > help them to automate some tasks (e.g., generate plots). This
> > narrow-minded view is no longer true. Computer-like symbolic
> > manipulation and recombination have inspired many psychologists and AI
> > researchers to question the sufficiency of the traditional artificial
> > neural networks (e.g., Minsky 1991). However, many neural network
> > researchers do not understand or even care about such questions,
> > simply disregarding them as "not my problem". The recent establishment
> > (weng 2010) that the base network of symbolic AI systems (i.e., FA) is
> > a special case of a brain-mind network DN indicates the necessity and
> > urgency for all researchers and students in EE, Psychology,
> > neuroscience, biology, and mathematics to learn computer science,
> > especially the automata theory and computational complexity theory. To
> > understand how the brain biology works, one must understand at least
> > how an automaton operates on symbols and how symbols are related to
> > meanings in computers. No, traditional AI theories are not close to
> > what the brain does, but they are necessary for understand how the
> > brain network does symbolic AI.
> >
> >
> > --
> > --
> > Juyang (John) Weng, Professor
> > Department of Computer Science and Engineering
> > MSU Cognitive Science Program and MSU Neuroscience Program
> > 3115 Engineering Building
> > Michigan State University
> > East Lansing, MI 48824 USA
> > Tel: 517-353-4388
> > Fax: 517-432-1061
> > Email:weng at cse.msu.edu
> > URL:http://www.cse.msu.edu/~weng/
> > ----------------------------------------------
> >
> >
> >
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