[Comp-neuro] Re: Attractors, variability and noise

Axel HUTT axel.hutt at loria.fr
Thu Aug 14 13:03:31 CEST 2008

Dear Andrew,

many thanks for this important contribution. 

In fact if one understands the single elements in the system (as in 
your case), you could shift between different levels of description 
and gain important insights. Hermann Haken, Scott Kelso, Paul Nunez 
and many others have worked for decades on the treatment of the brain as
 a complex system and tried to traverse the scales. As a result, today 
we can describe motor coordination on a macroscopic level as a
 nonlinear interaction of subsystems, which is reflected by MEG/EEG
or fMRI activity. See e.g.
Kelso, J.A.S., Fuchs, A., Holroyd, T., Lancaster, R., Cheyne, D., &
Weinberg, H. "Dynamic cortical activity in the human brain reveals motor
equivalence". Nature, 392, 814-818. (1998)


On Wed, 2008-08-13 at 10:32 -0700, Andrew Coward wrote:
> What we can or cannot understand is fundamental to both science and
> understanding complex commercial systems.
> For many years I was engaged in the design of very complex electronic
> systems. To give some idea of the complexity, it took the efforts of
> several  thousand engineers working for three or four years to design
> the orignial system (a telecommunications switch). Practical
> understanding of the system existed, in the sense that it was possible
> to diagnose and repair faults, add and modify features without
> undesirable side effects on other features etc. This understanding
> depended on the organization of the design into a hierarchy of
> descriptions on many different levels of description (functions could
> be described in terms of transistors, logic gates, standard cells
> within integrated circuits, integrated circuits, printed circuit
> boards, subsystems, and an analogous hierarchy for software).  A
> description of the complete system in terms of individual transistors
> would have an incomprehensible level of detail, but higher level
> descriptions are approximate to some degree. However, there are well
> understood routes to go from a more approximate higher level
> description to a more precise detailed description (for subsets of the
> phenomenon of interest so that comprehension is possible). Shifting
> between different levels of description makes it possible to achieve
> adequate levels of understanding.
> The physical sciences follow a very similar approach {Coward and Sun
> 2007]. There are no attempts to understand the movement of continents
> directly in terms of quantum mechanics. Rather, there are hierarchies
> of description (continental drift, geology, crystallography, and if
> necessary atomic theory and even quantum mechanics). The higher levels
> are more approximate, but if higher precision were essential in some
> domain, a more exact, detailed description is possible.  It is
> relevant that even research at the borders of quantum mechanics starts
> with "purely classical language that ignores quantum probabilities,
> wave functions and so forth ..... subsequently overlaying quantum
> concepts upon a classical framework" [Greene, 1999, page 380].
> Understanding the brain will require an analogous hierarchy of
> descriptions. In other words, we have to find good approximations at
> several intermediate levels that can be mapped both into psychology
> and into more detailed neuron type models. One attempt at such a
> multilevel theory is [Coward 2005]. The danger of massive neuron
> modelling efforts is that we can create a system which may have
> properties similar to brains but do not help with genuine intellectual
> understanding.

> Andrew Coward
> Coward, L. A. and Sun, R. (2007). Hierarchical Approaches to
> Understanding Consciousness. Neural Networks 20(9), 947 - 954.
> Greene, G. (1999). The Elegant Universe. Norton.
> Coward, L. A. (2005). A System Architecture Approach to the Brain:
> from Neurons to Consciousness. New York: Nova Science Publishers.

Axel Hutt
INRIA CR Nancy - Grand Est
CS20101, 54603 Villers-lès-Nancy Cedex

More information about the Comp-neuro mailing list