How can an engineer learn from neuroscientists

junior1 at ibm.net[Bernie Arruza] junior1 at ibm.net[Bernie Arruza]
Wed Mar 5 20:55:04 EST 1997


In <mitchm-0403970037170001 at scottsdale-ts5-7.goodnet.com>, mitchm at netzone.com (Mitchell Gil Maltenfort) writes:
>
>The reason neurophysiologists are interested in models is that we often
>can *not* work with the real thing...for one reason or another, the
>experiments are not feasible to perform. For example, my own work was
>looking at the ensemble behavior of a few hundred neurons - in the
>physiological case, it's only possible to record from a few neurons
>simultaneously.  (Composite signals like the EEG are indirect
>measures).     
>
>Think of physics, where theoretical analysis became more prominent as the
>particles became harder to get at.
>
>The question the neurophysiologists will ask are better phrased "Can we
>represent the known behavior of each component of the system, and the
>connections between components, so that we can simulate the system and
>observe its behavior?  Does the simulation then produce the important
>behaviors expected from the physiological system, or do we need to develop
>a better understanding of the structure so we can improve the model?"  
>
>For a neuron, the components might be the ion channels, the cell membrane,
>the dendritic tree.  In a reflex, the components might be the sensors
>(say, pain receptors), the afferent fibers carrying information to
>interneurons or to neurons innervating the muscle, the interneurons and
>the motor neurons.  
>
>I say 'might be' because the relevant details might change depending on
>the question the scientist wants to ask and the researcher's own opinion
>of what features are important.  Can a population of neurons be lumped
>into a single mathematical function?  Can firing rate be represented as a
>continuous-valued function, or is the timing of neural firings important? 
>Do we need to represent the dendritic tree in detail or can we use an
>equivalent cylinder description?  
>
>Neurophysiology is like any other engineering problem in this respect: you
>have to be sure you define the constraints of the solution appropriately.
 Again, a million thanks. This is very interesting. If I were to sum up what you
are saying (I hope I'm right), when you build your systems with the help of
neuroscientists you use inductive techniques to come up with your design
instead of deductive techniques. 
 A programmer building an expert system with the help of a neuroscientist
would attempt to specify rules as fully as possible to present the user of
the Expert System with a reasonable outcome. he would tweak the rule set
to optimize the solution (deductive approach). 
 In your case, since you know more about specific behaviors than about the
group behavior, you specify the problem as a set of known behaviors (inductive
approach).  
 You probably know this, but this technique is also used for the creation of
Artificial Neural Networks (ANNs). The beauty of this approach is that the 
solution may surprise you (a breakthrough). Not a chance that this could 
happen using the deductive method (at best you missed the solution,
but it was there, in front of your nose).  


___________________________________
Bernie Arruza.
IBM
Boca Raton, Fl USA
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