Connectionists: Best practices in model publication

[james bower]

The diminished role of true predictions in neuroscience is, obviously, another reflection of the lack of a computational base for the field.  The vast majority of neuroscience papers are data descriptions, usually with a nod to something that sounds vaguely hypothesis or theory like in the introduction and discussion.  In many cases, in fact, the attempts to tie the work into some larger context are laughable.  

However, modelers and theorists don’t help the situation by loose use of the word ‘prediction’ either.  Perhaps we should all adopt the distinction between “explanation of findings” and true predictions Shimon makes.  While in the limit this is a complex epistemological issue that philosophers of science have debated for many years, in practical application day to day it is, I think, often a pretty clear distinction.  

Sadly, under current circumstances, it seems to me that many theorists and modelers, to get any attention, feel the need to assert the importance of their work by listing all its “predictions”.  I have sat next to many an experimentalist in workshops and meetings who just roll their eyes at these kinds of presentations.

Jim



On Jan 30, 2014, at 8:37 AM, Shimon Edelman <edelman at cornell.edu> wrote:

> 
> On Jan 30, 2014, at 12:07 AM, Brad Wyble <bwyble at gmail.com> wrote:
> 
>> Thanks for your input John, I appreciate it extremely.  One of the points that I think is perhaps most important to improve on in our field is the scope of the predictions.  In physics, predictions seem to occur on a much grander scale, and very inspiring, and this itself engenders respect for the theoretical work.  
>> 
>> In neuroscience, the predictions are expected to be easily testable, which leads necessarily to predictions that are smaller in scope, and testable with technology that is already at hand.  
>> 
>> -Brad
> 
> 
> Speaking of predictions, here’s one, regarding the veridicality of the representation of some metric properties of the world in brains. In several papers and one book, published between 1994 and 1999, I predicted (from certain mathematical properties of smooth functions) that the ensemble activity of graded responses of neurons in the primate inferotemporal cortex will be found to capture similarity patterns over families of 3D shapes. Here’s how the prediction was formulated in my 1998 BBS paper:
> 
> 
> "1. The cell will respond equally to different views of its
> preferred object, but its response will decrease with parameter-
> space distance from the point corresponding the
> shape of the preferred object (three such cells have been
> reported by Logothetis et al. 1995).
> 2. The responses of a number of cells, each tuned to a
> different reference object, will carry enough information to
> classify novel stimuli of the same general category as the
> reference objects.
> 3. If the pattern of stimuli has a simple low-dimensional
> characterization in some underlying parameter space (as in
> Fig. 6, left), it will be recoverable from the ensemble response
> of a number of cells, using multidimensional scaling.”
> 
> Prediction #1 was really an explanation of some findings that just began to emerge when it was formulated. Predictions #2 and #3, however, were really about the future :-)
> I am particularly fond of #3. A couple of years later, it was tested and found correct ("Inferotemporal neurons represent low-dimensional configurations of parameterized shapes”, Hans Op de Beeck, Johan Wagemans and Rufin Vogels, Nature Neuroscience 4:1244, 2001). Funny enough, that paper opened with this sentence: "Behavioral studies with parameterized shapes have shown that the similarities among these complex stimuli can be represented using a low number of dimensions.” — apparently, a theoretical prediction wasn’t good enough; they (or, more likely, the Nat Neuro reviewers) preferred to kick off an empirical finding, as if that had existed in a theoretical vacuum…
> 
> Unless I am mistaken in my interpretation of the stances expressed by different contributors to this thread, this kind of prediction is supposed, on at least one account, to be impossible/imprudent/whatever in trying to understand the brain. Yet, here it is: veridicality as a mathematically guaranteed generic property of neural representations. I guess sticking exclusively to Genesis at the expense of an occasional glimpse at what Prophets are up to may not be the most productive way ahead, after all ;-)
> 
> Cheers,
> 
> —Shimon
> 
> p.s. My papers are all available here:
> http://kybele.psych.cornell.edu/~edelman/archive.html
> 
> 
> Shimon Edelman
> Professor, Department of Psychology, 232 Uris Hall
> Cornell University, Ithaca, NY 14853-7601
> http://kybele.psych.cornell.edu/~edelman
> @shimonedelman
> 
> 
> 
> 

 

 

Dr. James M. Bower Ph.D.

Professor of Computational Neurobiology

Barshop Institute for Longevity and Aging Studies.

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