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Penultimate version for Oxford Handbook of Philosophy and Neuroscience, John Bickle (ed). Neurocomputational models: Theory, application, philosophical consequences
 

Summary: Penultimate version for Oxford Handbook of Philosophy and Neuroscience, John Bickle (ed).
Neurocomputational models: Theory, application, philosophical consequences
Chris Eliasmith, University of Waterloo
Abstract: Neural coding, neural computation, and control (or dynamical systems) theory have
recently been integrated to support descriptions of neurobiological systems at many different
levels of detail. I discuss the Neural Engineering Framework (NEF), which realizes this
integration, and highlight some of the philosophical consequences of its development. To do so, I
present the three principles of the NEF, and then describe an example of their application to the
development of a neural model of part of the rat navigation system. Relying partly on the
insights provided by this model, I describe the implications of the NEF for traditional
philosophical problems including: 1) mental representation and semantics; 2) the unity of science
(i.e., theory reduction); and 3) appropriate theory construction in the behavioural sciences.
Keywords: theoretical neuroscience, computational neuroscience, neural coding, neural
modeling, neural computation, representation, neuroscience, semantics, rodent navigation, path
integration, unity of science
1. Introduction
Theoretical (or computational) neuroscience has come to play a role in neuroscience akin
to that played by theoretical physics in the physical sciences. However, unlike theoretical
physics, theoretical neuroscience is not characterized by a few well-studied basic theories (e.g.,
string theory, loop quantum gravity, etc.). Instead, as can be seen by perusing the textbooks in

  

Source: Anderson, Charles H. - Departments of Anatomy and Neurobiology & Physics, Washington University in St. Louis

 

Collections: Computer Technologies and Information Sciences; Biology and Medicine