Connectionists: how the brain works?

[Brian J Mingus]

Jim,

I feel like something is missing here. The Brain is information. Knowing
its first principal component would be a *huge* advance for neuroscience.
The principle goal of the Ockham gradient is to first identify this
component.

The theoretical basis for doing this is quite solid - I mean it's basically
just hill climbing and it's honestly quite hard to reasonably dispute.
Disputing it on the grounds of specific edge cases strikes me as
confirmation bias. *If we can create a mean field theory model of the brain
that reinvents consciousness philosophy, our principal work is done, even
though we didn't include anything about Purkinje cells!*

Since you mentioned you appreciate my honesty, I hope you don't mind a
little more. :) The graident of abstract to specific (or, in the case of
this thread, genotype to phenotype) is a very principled and good one. The
actual gradient society is following - throw in in all the data from all
the approaches into a pot and *then* do a PCA - is also going to work. It's
just an inefficient waste of time when we could be taking a much more
principled and effective approach.

Brian


On Wed, Mar 19, 2014 at 8:33 PM, james bower <bower at uthscsa.edu> wrote:

> Geoffrey,
>
> Nice addition to the discussion actually introducing an interesting angle
> on the question of brain organization (see below)  As you note, reaction
> diffusion mechanisms and modeling have been quite successful in replicating
> patterns seen in biology - especially interesting I think is the modeling
> of patterns in slime molds, but also for very general pattern formation in
> embryology.  However, more and more detailed analysis of what is diffusing,
> what is sensing what is diffusing, and what is reacting to substances once
> sensed -- all linked to complex patterns of gene regulation and expression
> have made it clear that actual embryological development is much much more
> complex, as Turing himself clearly anticipated, as the quote you cite
> pretty clearly indicates.   Clearly a smart guy.   But, I don't actually
> think that this is an application of Ochham's razor although it might
> appear to be after the fact.  Just as Hodgkin and Huxley were not applying
> it either in their model of the action potential.   Turing apparently
> guessed (based on a lot of work at the time on pattern formation with
> reaction diffusion) that such a mechanism might provide the natural basis
> for what embryos do. Thus, just like for Hodgkin and Huxley, his model
> resulted from a bio-physical insight, not an explicit attempt to build a
> stripped down model for its own sake.  I  seriously doubt that Turning
> would have claimed that he, or his models could more effectively do what
> biology actually does in forming an embrio, or substitute for the actual
> process.
>
> However, I think there is another interesting connection here to the
> discussion on modeling the brain. Almost certainly communication and
> organizational systems in early living beings were reaction diffusion
> based.  This is still a dominant effect for many 'sensing' in small
> organisms.  Perhaps, therefore, one can look at nervous systems as
> structures specifically developed to supersede reaction diffusion
> mechanisms, thus superseding this very 'natural' but complexity limited
> type of communication and organization.  What this means, I believe, is
> that a simplified or abstracted physical or mathematical model of the brain
> explicitly violates the evolutionary pressures responsible for its
> structure.  Its where the wires go, what the wires do, and what the
> receiving neuron does with the information that forms the basis for neural
> computation, multiplied by a very large number.  And that is dependent on
> the actual physical structure of those elements.
>
> One more point about smart guys,  as a young computational neurobiologist
> I questioned how insightful John von Neumann actually was because I was
> constantly hearing about a lecture he wrote (but didn't give) at Yale
> suggesting that dendrites and neurons might be digital ( John von
> Neumann's *The Computer and the Brain*. (New Haven/London: Yale Univesity
> Press, 1958.)  Very clearly a not very insightful idea for a supposedly
> smart guy.  It wasn't until a few years later, when I actually read the
> lecture - that I found out that he ends by stating that this idea is almost
> certainly wrong, given the likely nonlinearities in neuronal dendrites.  So
> von Neumann didn't lack insight, the people who quoted him did.  It is a
> remarkable fact that more than 60 years later, the majority of models of so
> called neurons built by engineers AND neurobiologists don't consider these
> nonlinearities.  The point being the same point, to the Hopfield, Mead,
> Feynman list, we can now add Turing and von Neumann as suspecting that for
> understanding,  biology and the nervous system must be dealt with in their
> full complexity.
>
> But thanks for the example from Turing - always nice to consider actual
> examples.   :-)
>
> Jim
>
>
>
>
>
> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill <g.goodhill at uq.edu.au>
> wrote:
>
> Hi All,
>
> A great example of successful Ockham-inspired biology is Alan Turing's
> model for pattern formation (spots, stripes etc) in embryology (The
> chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing
> introduced a physical mechanism for how inhomogeneous spatial patterns can
> arise in a biological system from a spatially homogeneous starting point,
>  based on the diffusion of morphogens. The paper begins:
>
> "In this section a mathematical model of the growing embryo will be
> described. This model will be a simplification and an idealization, and
> consequently a falsification. It is to be hoped that the features retained
> for discussion are those of greatest importance in the present state of
> knowledge."
>
> The paper remained virtually uncited for its first 20 years following
> publication, but since then has amassed 8000 citations (Google Scholar).
> The subsequent discovery of huge quantities of molecular detail in
> biological pattern formation have only reinforced the importance of this
> relatively simple model, not because it explains every system, but because
> the overarching concepts it introduced have proved to be so fertile.
>
> Cheers,
>
> Geoff
>
>
> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote:
>
> Ignoring the gross differences in circuitry between hippocampus and
> cerebellum, etc., is not erring on the side of simplicity, it is erring,
> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of
> their circuitry?
>
> At 01:07 PM 3/19/2014, Brian J Mingus wrote:
>
> Hi Jim,
>
> Focusing too much on the details is risky in and of itself. Optimal
> compression requires a balance, and we can't compute what that balance is
> (all models are wrong). One thing we can say for sure is that we should err
> on the side of simplicity, and adding detail to theories before simpler
> explanations have failed is not Ockham's heuristic. That said it's still in
> the space of a Big Data fuzzy science approach, where we throw as much data
> from as many levels of analysis as we can come up with into a big pot and
> then construct a theory. The thing to keep in mind is that when we start
> pruning this model most of the details are going to disappear, because
> almost all of them are irrelevant. Indeed, the size of the description that
> includes all the details is almost infinite, whereas the length of the
> description that explains almost all the variance is extremely short,
> especially in comparison. This is why Ockham's razor is a good heuristic.
> It helps prevent us from wasting time on unnecessary details by suggesting
> that we only inquire as to the details once our existing simpler theory has
> failed to work.
>
> On 3/14/14 3:40 PM, Michael Arbib wrote:
>
> At 11:17 AM 3/14/2014, Juyang Weng wrote:
>
> The brain uses a single architecture to do all brain functions we are
> aware of!  It uses the same architecture to do vision, audition, motor,
> reasoning, decision making, motivation (including pain avoidance and
> pleasure seeking, novelty seeking, higher emotion, etc.).
>
>
> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were
> very different from each other.
>
>
>
>
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