Connectionists: "Abstract" vs "Biologically realistic" modelling

Wed Jan 29 11:23:56 EST 2014

I bet you guys wish I still had to attend faculty meetings, meet with the various dean-lits about new safety measures for the laboratory and spend hours talking about ways to reorganize the graduate program given new budgetary constraints.  I also never have to have another discussion about the ROI for every square inch of my laboratory.  he he.

Thus, returning to intellectual pursuits  :-)  :

In addition to the on-list conversation, there has also been a robust off list conversation going on. Remarkably enough, it turns out that several members of the list have actually taken the time to read the paper on Purkinje cells that I linked to - and several have made the valid point that work on the cerebellum has the considerable benefit that we actually know a lot (and have for more than 100 years) about its basic architecture.

Which raises another issue.

To return to Newton.  From historical documents, it would appear, as I have said, that a key insight that eventually drove his work on mechanics, was obtained when he was quite young, by examining celestial mechanics with what was, in effect, a ‘realistic’ model of the moon orbiting the earth.  He did not, as Kepler had, for example, taken on the problem of planetary motion - instead he chose a system with quite good data, and importantly, a case in which the orbit was actually almost circular.  Not coincidentally, there has never been any debate but that the moon actually does revolve around the earth.

In other words, he chose the right system to ask the question.  

In neuroscience and computational neuroscience in particular, the majority of the models theories and even experimental work involves the visual system.  The reason is probably obvious, as we value ours a lot.  However, as several off list have pointed out I think accurately, we still to this day know much less about the visual system’s architecture than we do about that of the cerebellum or for example 3 layered cortex like that used by the olfactory system.   

Not to be controversial, but, if I were NIH - I would suspend funding work on the visual system, or neo-cortex in general, and get the field to focus on ‘archi-cortex’ and the systems that use it (olfaction being the primary one).  I would also suspend work on the hippocampus, until we actually understand the role the olfactory system played in the evolution and even current function of that structure.  In fact, from the point of view of comparative mammalian studies - the olfactory system is much more behaviorally important (even for us) than the visual system anyway.  (have you ever gone out on a date twice with someone whose smell you found offensive - nope).  Furthermore, there are good reasons to believe  that the olfactory system “invented” the fundamental architecture of cerebral cortical networks.  Accordingly, I have suggested for many years that if you want to understand how cerebral cortex works, you first need to understand this structure in the context of the sensory system that invented it (olfaction),  not the ones that parasitized it (e.g. vision).   

I can provide papers for anyone interested.

So, NOW not only am I telling you HOW to study the brain, and I am also telling you what parts of the brain it makes the most sense to study, given the current state of neuroscience.  :-)

(Does his arrogance know no bounds  :-)  ).

In biology, the right choice of system (squid axon, lobster somatogastric system, Tritonia swim system, cerebellum, olfactory system) has always been critically important to make progress.

Jim

On Jan 28, 2014, at 9:53 PM, Brad Wyble <bwyble at gmail.com> wrote:

> I'd like to throw in a few cents. 
>  
>  However, if you want to claim that your model also reveals something important about how brains work, then the model must either be ‘realistic’ first, or be able to link to such a model.
> 
> 
> I think that whether this is true depends on your definition of "how brains work".  For my purposes, the litmus test of a model that reflects "how brains work" in some fundamental sense is reflected in their ability to generate novel predictions that are correct.  Your warnings about postdiction are right on target, since the predictions must be truly de novo at the time they are created to ensure that you are not engaging in curve fitting. 
> 
> Therefore to me it seems as if we are using very different goals in the modelling process. I want a model to reflect the behavior of the system, and I use neural models because the added constraint of neural plausibility enormously accelerates my search through the space of possible models by preventing me from following innumerable dead ends (though there are still quite a lot of neurally plausible dead ends).  
> 
> Jim, you seem to want a model that reflects the wiring of the brain first and foremost, and the functionality comes in a close second.  I think it's fine for both of those goals to exist and I don't think it's necessary to label one of them as useless, or even less efficient.  
> 
> -Brad 
> 
> PS.  Thanks for a very interesting debate!
>   
> 
> 
> 
> 
> 
> 
>  
> It goes without saying that these types of realistic models can be built at many levels, as long as the model has biological components.  (you won’t convince me with mean field theories of cerebral cortex).  It also goes without saying, of course, that we don’t have the technology or the knowledge for that matter to build one model of everything - although personally, I believe eventually we will have to, and reflecting that view, Version 3.0 of GENESIS was specifically built to link broadly across many different levels of scale.
> 
> The critical question therefore, is whether the model is built in such a way that the biology can tell you something you didn’t know before you started (just like the earth moon model told Newton) - or, is the biology just dressing up something you already believed to be true and just wanted to convince the rest of us.  Building the model out of realistic components, and then testing it on theory- neutral biological data, is more likely to lead to the former.  At least it has over and over again for us.
> 
> 
> Jim
> 
> 
> 
>> and in particular that your type of "realistic"
>> multicompartmental single-cell and network modelling could ever do so.
>> 
>> *Real* morphologically complex cells are embedded in complex networks,
>> which are embedded in complex organisms, which are embedded in complex
>> environments, which are embedded in complex ecosystems.  Evolution
>> acts on the net result of *all* of this, indirectly via a process of
>> development.  Certain species thrive in certain ecosystems if their
>> proteins, cells, networks, nervous systems, bodies, and communities
>> allow them to function in that environment well enough to reproduce.
>> The details of *all* of these things matter.
>> 
>> Are all of these details represented realistically in your models?
>> No, and they shouldn't be -- you pose questions that can be addressed
>> by the things you do include, abstract away the rest, and all is well
>> and good.  But other different yet no less realistic models are built
>> to address different questions, paying attention to different sets of
>> details (such as large-scale development and plasticity, for my own
>> models), and again abstract away the rest.
>> 
>> I am happy to join with you to decry truly unrealistic models, which
>> would be those that respect none of the details at any level.  Down
>> with unrealistic models!  But there is no meaningful sense in which
>> any model can be claimed to avoid abstraction, and no level that
>> exclusively owns biological realism.
>> 
>> Jim Bednar
>> 
>> ________________________________________________
>> 
>> Dr. James A. Bednar
>> Director, Doctoral Training Centre in 
>> Neuroinformatics and Computational Neuroscience
>> University of Edinburgh School of Informatics
>> 10 Crichton Street, Edinburgh, EH8 9AB  UK
>> http://anc.ed.ac.uk/dtc
>> http://homepages.inf.ed.ac.uk/jbednar
>> ________________________________________________
>> 
>> -- 
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>> 
> 
>  
> 
>  
> 
> 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
> 
> Email: bower at uthscsa.edu
> 
> Web: http://www.bower-lab.org
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> twitter: superid101
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> linkedin: Jim Bower
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> -- 
> Brad Wyble
> Assistant Professor
> Psychology Department
> Penn State University
> 
> http://wyblelab.com

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

Email: bower at uthscsa.edu

Web: http://www.bower-lab.org

twitter: superid101

linkedin: Jim Bower

CONFIDENTIAL NOTICE:

The contents of this email and any attachments to it may be privileged or contain privileged and confidential information. This information is only for the viewing or use of the intended recipient. If you have received this e-mail in error or are not the intended recipient, you are hereby notified that any disclosure, copying, distribution or use of, or the taking of any action in reliance upon, any of the information contained in this e-mail, or

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immediately returned to the sender or destroyed and, in either case, this e-mail and all attachments to this e-mail must be immediately deleted from your computer without making any copies hereof and any and all hard copies made must be destroyed. If you have received this e-mail in error, please notify the sender by e-mail immediately.

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