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Space-Time Computing with Temporal Neural Networks (Paperback)
Loot Price: R1,471
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Space-Time Computing with Temporal Neural Networks (Paperback)
Series: Synthesis Lectures on Computer Architecture
Expected to ship within 10 - 15 working days
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Understanding and implementing the brain's computational paradigm
is the one true grand challenge facing computer researchers. Not
only are the brain's computational capabilities far beyond those of
conventional computers, its energy efficiency is truly remarkable.
This book, written from the perspective of a computer designer and
targeted at computer researchers, is intended to give both
background and lay out a course of action for studying the brain's
computational paradigm. It contains a mix of concepts and ideas
drawn from computational neuroscience, combined with those of the
author. As background, relevant biological features are described
in terms of their computational and communication properties. The
brain's neocortex is constructed of massively interconnected
neurons that compute and communicate via voltage spikes, and a
strong argument can be made that precise spike timing is an
essential element of the paradigm. Drawing from the biological
features, a mathematics-based computational paradigm is
constructed. The key feature is spiking neurons that perform
communication and processing in space-time, with emphasis on time.
In these paradigms, time is used as a freely available resource for
both communication and computation. Neuron models are first
discussed in general, and one is chosen for detailed development.
Using the model, single-neuron computation is first explored.
Neuron inputs are encoded as spike patterns, and the neuron is
trained to identify input pattern similarities. Individual neurons
are building blocks for constructing larger ensembles, referred to
as "columns". These columns are trained in an unsupervised manner
and operate collectively to perform the basic cognitive function of
pattern clustering. Similar input patterns are mapped to a much
smaller set of similar output patterns, thereby dividing the input
patterns into identifiable clusters. Larger cognitive systems are
formed by combining columns into a hierarchical architecture. These
higher level architectures are the subject of ongoing study, and
progress to date is described in detail in later chapters.
Simulation plays a major role in model development, and the
simulation infrastructure developed by the author is described.
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