In conventional information systems uncertainty in information is
regarded as "noise", and treated as a problem. In neural networks,
however, uncertainty is an intrinsic feature of much of the
information processing, with many of the computations involving
the deliberate injection of stochastic processes. This paper
analyzes uncertainty in information representation and processing
in terms of the benefits arising, their theoretical foundations,
their practical applications, and their hardware implementation. It
gives a new perspective on network computations and the information
involved. As an example of this approach the paper analyzes the
representation of analog quantities by stochastic sequences of bits.
Benefits of this approach are both theoretical and practical.
Realization of communication links between units is greatly
simplified and the number of wires needed in interconnecting
VLSI chips correspondingly reduced. The hardware realization of
basic operations on analog quantities is very simple, for example,
in some cases a single and-gate can be used as a multiplier. The
stochastic approach also gives elegant solutions to problems
including learning, representation of system controllers, uncertain
inference, and partial differential equations.
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