What is your doctor going to do with this to help your recovery?
ANYTHING AT ALL?
http://journal.frontiersin.org/article/10.3389/fncom.2016.00094/abstract?
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1Media Lab, Massachusetts Institute of Technology, USA
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2Google Deepmind, United Kingdom
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3Northwestern University, USA
Neuroscience has focused on the detailed implementation of
computation, studying neural codes, dynamics and circuits. In machine
learning, however, artificial neural networks tend to eschew precisely
designed codes, dynamics or circuits in favor of brute force
optimization of a cost function, often using simple and relatively
uniform initial architectures. Two recent developments have emerged
within machine learning that create an opportunity to connect these
seemingly divergent perspectives. First, structured architectures are
used, including dedicated systems for attention, recursion and various
forms of short- and long-term memory storage. Second, cost functions and
training procedures have become more complex and are varied across
layers and over time. Here we think about the brain in terms of these
ideas. We hypothesize that (1) the brain optimizes cost functions, (2)
the cost functions are diverse and differ across brain locations and
over development, and (3) optimization operates within a pre-structured
architecture matched to the computational problems posed by behavior. In
support of these hypotheses, we argue that a range of implementations
of credit assignment through multiple layers of neurons are compatible
with our current knowledge of neural circuitry, and that the brain's
specialized systems can be interpreted as enabling efficient
optimization for specific problem classes. Such a heterogeneously
optimized system, enabled by a series of interacting cost functions,
serves to make learning data-efficient and precisely targeted to the
needs of the organism. We suggest directions by which neuroscience could
seek to refine and test these hypotheses.
Adam H. Marblestone1*, Greg Wayne2 and
Konrad Kording3 
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