Connectionists: Brain-like computing fanfare and big data fanfare

[Brad Wyble]

Jim,

Great debate!  There are several good points here..

First, I agree with you that models with tidy, analytical solutions are
probably not the ultimate answer, as biology is unlikely to exhibit
behavior that coincides with mathematical formalisms that are easy to
represent in equations. In fact, I think that seeking such solutions can
get in the way of progress in some cases.

I also agree with you that community models are a good idea, and I am not
advocating that everyone should build their own model.  But I think that we
need a hierarchy of such community models at multiple levels of
abstraction, with clear ways of translating ideas and constraints from each
level to the next.  The goal of computational neuroscience is not to build
the ultimate model, but to build a shared understanding in the minds of the
entire body of neuroscientists with a minimum of communication failures.

Next,  I think that you're espousing a purely bottom-up approach to
modelling the brain. ( i.e. that if we just build it, understanding will
follow from the emergent dynamics). I very much admire your strong
position, but I really can't agree with it.  I return to the question of
how we will even know what the bottom floor is in such an approach  You
seem to imply in previous emails that it's a channel/cable model, but
someone else might argue that we'd have to represent interactions at the
atomic level to truly capture the dynamics of the circuit.  So if that's
the only place to start, how will we ever make serious progress?  The
computational requirements to simulate even a single neuron at the atomic
level on a super cluster is probably a decade away. And once we'd
accomplished that, someone might point out a case in which subatomic
interactions play a functional role in the neuron and then we've got to
wait another 10 years to be able to model a single neuron again?

To me, it really looks like turtles all the way down which means that we
have to choose our levels of abstraction with an understanding that there
are important dynamics at lower levels that will be missed.  However if we
build in constraints from the behavior of the system, such abstract models
can nevertheless provide a foothold for climbing a bit higher in our
understanding.

Is there some reason that you think channels are a sufficient level of
detail?  (or maybe I've mischaracterized your position)

-Brad





On Sat, Jan 25, 2014 at 7:09 PM, james bower <bower at uthscsa.edu> wrote:

> About to sign off here - as have probably already taken too much
> bandwidth. (although it has been a long time)
>
> But just for final clarity on the point about physics - I am not claiming
> that the actual tools etc, developed by physics mostly to study
> non-biological and mostly ‘simpler’ systems (for example, systems were the
> elements (unlike neurons) aren’t ‘individualized’  - and therefore can be
> subjected to a certain amount of averaging (ie. thermodynamics), will apply.
>
> But I am suggesting (all be it in an oversimplified way)  that the
> transition from a largely folkloric, philosophically (religiously) driven
> style of physics, to the physics of today was accomplished in the 15th
> century by the rejection of the curve fitting, ‘simplified’ and self
> reflective Ptolemic model of the solar system. (not actually, it turns out
> for that reason, but because the Ptolemaic model has become too complex and
> impure - the famous equint point).   Instead, Newton, Kepler, etc, further
> developed a model that actually valued the physical structure of that
> system, independent of the philosophical, self reflecting previous set of
> assumptions.  I know, I know that this is an oversimplified description of
> what happened, but, it is very likely that Newtons early (age 19) discovery
> of what approximated the least squares law in the ‘realistic model’ he had
> constructed of the earth moon system (where it was no problem and pretty
> clearly evident that the moon orbited the earth in a regular way), lead in
> later years to his development of mechanics - which, clearly provided an
> important "community model” of the sort we completely  lack in neuroscience
> and seem to me continue to try to avoid.
>
> I have offered for years to buy the beer at the CNS meeting if all the
> laboratories describing yet another model of the hippocampus or the visual
> cortex would get together to agree on a single model they would all work
> on.  No takers yet.  The paper I linked to in my first post describes how
> that has happened for the Cerebellar Purkinje cell, because of GENESIS and
> because we didn’t block others from using the model, even to criticize us.
>    However, when I sent that paper recently to a computational neuroscience
> I heard was getting into Purkinje cell modeling, he wrote back to say he
> was developing his own model thank you very much.
>
> The proposal that we all be free to build our own models - and everyone is
> welcome, is EXACTLY the wrong direction.
>
> We need more than calculous - and although I understand their
> attractiveness believe me, models that can be solved in close formed
> solutions are not likely to be particularly useful in biology, where the
> averaging won’t work in the same way. The relationship between scales is
> different, lots of things are different - which means the a lot of the
> tools will have to be different too. And I even agree that some of the
> tools developed by engineering, where one is actually trying to make things
> that work, might end up being useful, or even perhaps more useful.
>  However, the transition to paradigmatic science I believe will critically
> depend on the acceptance of community models (they are the ‘paradigm’), and
> the models most likely with the most persuasive force as well as the ones
> mostly likelihood of revealing unexpected functional relationships, are
> ones that FIRST account for the structure of the brain, and SECOND are used
> to explore function (rather than what is usually the other way around).
>
> As described in the paper I posted, that is exactly what has happened
> through long hard work (since 1989) using the Purkinje cell model.
>
> In the end, unless you are a duelist (which I suspect many actually are,
> in effect), brain computation involves nothing beyond the nervous system
> and its physical and physiological structure.  Therefore, that structure
> will be the ultimate reference for how things really work, no matter what
> level of scale you seek to describe.
>
> From 30 years of effort, I believe even more firmly now than I did back
> then, that, like Newton and his friends, this is where we should start -
> figuring out the principles and behavior from the physics of the elements
> themselves.
>
> You can claim it is impossible - you can claim that models at other levels
> of abstraction can help, however, in the end ‘the truth’ lies in the
> circuitry in all its complexity.  But you can’t just jump into the
> complexity, without a synergistic link to models that actually provide
> insights at the detailed level of the data you seek to collect.
>
> IMHO.
>
> Jim
>
> (no ps)
>
>
>
>
>
>
>
> On Jan 25, 2014, at 4:44 PM, Dan Goodman <dg.connectionists at thesamovar.net>
> wrote:
>
> The comparison with physics is an interesting one, but we have to remember
> that neuroscience isn't physics. For a start, neuroscience is clearly much
> harder than physics in many ways. Linear and separable phenomena are much
> harder to find in neuroscience, and so both analysing and modelling data is
> much more difficult. Experimentally, it is much more difficult to control
> for independent variables in addition to the difficulty of working with
> living animals.
>
> So although we might be able to learn things from the history of physics -
> and I tend to agree with Axel Hutt that one of those lessons is to use the
> simplest possible model rather than trying to include all the biophysical
> details we know to exist - while neuroscience is in its pre-paradigmatic
> phase (agreed with Jim Bower on this) I would say we need to try a diverse
> set of methodological approaches and see what wins. In terms of funding
> agencies, I think the best thing they could do would be to not insist on
> any one methodological approach to the exclusion of others.
>
> I also share doubts about the idea that if we collect enough data then
> interesting results will just pop out. On the other hand, there are some
> valid hypotheses about brain function that require the collection of large
> amounts of data. Personally, I think that we need to understand the
> coordinated behaviour of many neurons to understand how information is
> encoded and processed in the brain. At present, it's hard to look at enough
> neurons simultaneously to be very sure of finding this sort of coordinated
> activity, and this is one of the things that the HBP and BRAIN initiative
> are aiming at.
>
> Dan
>
>
>
>
>
>
> Dr. James M. Bower Ph.D.
>
> Professor of Computational Neurobiology
>
> Barshop Institute for Longevity and Aging Studies.
>
> 15355 Lambda Drive
>
> University of Texas Health Science Center
>
> San Antonio, Texas  78245
>
>
>
> *Phone:  210 382 0553 <210%20382%200553>*
>
> Email: bower at uthscsa.edu
>
> Web: http://www.bower-lab.org
>
> twitter: superid101
>
> linkedin: Jim Bower
>
>
>
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-- 
Brad Wyble
Assistant Professor
Psychology Department
Penn State University

http://wyblelab.com
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