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Neuroscience and AI Share the Same Elegant Mathematical Trap Tsvi Achler, Eyal Amir
 

Summary: Neuroscience and AI Share the Same Elegant Mathematical Trap
Tsvi Achler, Eyal Amir
University of Illinois at Urbana-Champaign
201 N. Goodwin Ave, Urbana IL 61801, USA
Abstract
Animals display exceptionally robust recognition abilities to
analyze scenes compared to artificial means. The prevailing
hypothesis in both the neuroscience and AI literatures is that
the brain recognizes its environment using optimized
connections. These connections are determined through a
gradual update of weights mediated by learning. The
training and test distributions can be constrained to be
similar so weights can be optimized for any arbitrary
pattern. Thus both fields fit a mathematical-statistical
framework that is well defined and elegant.
Despite its prevalence in the literature, it remains difficult to
find strong experimental support for this mechanism within
neuroscience. Furthermore this approach is not ideally
optimized for novel combinations of previously learned
patterns which typically form a scene. It may require an

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences