Although the density and speed of integrated
circuits has grown exponentially during the last decades, so too have costs
of fabrication and test facilities. Current computational methods depend on
completely reliable hardware, a constraint that greatly increases the degree
of fabrication precision required to avoid failure of individual components,
and also increases the amount of post-fabrication testing required to
confirm correct function. Nature has solved these production problems.
The neocortex, is a cellular computer that generates intelligent behaviour.
But more than this, it constructs and configures itself by replication of a
few precursor cells. Each derived cell is equipped with a set of related
rules inherited from its functional parents, and by each cell implementing
these locally, the overall cell mass is able to achieve global coherent
action. Harnessing these principles for artificial fabrication would
revolutionize computer technology. Here we propose some first steps towards
understanding these developmental construction mechanisms. We will
demonstrate, by a fusion of experimental neuroscience, detailed physical
simulation, and theoretical analysis, the principles by which a population
of real or artificial neurons can grow and assemble themselves into
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We will apply these principles by engineering some first self-constructing
applications, in which a designed genetic code is inserted into a precursor
cell, and so initiates a developmental process of cell division, cell
migration, neurite growth, and synaptogenesis. The final global organization
of self-constructed neural networks appears to be an attractor in which
component neurons are able to satisfy their own local organizational
objectives. Thus, unlike existing artificial processing systems, we expect
our self-constructed networks to respond to damage or environmental changes
by significant self-repair and axonal re-wiring.