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NeuroCOLT
Technical Report NC-TR-96-013
Lower Bounds for
the Computational Power of Networks of Spiking Neurons
Wolfgang
Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
Austria
Abstract
We investigate the computational power of a formal model for networks
of spiking neurons. It is shown that simple operations on phase-differences
between spike-trains provide a very powerful computational tool that
can in principle be used to carry out highly complex computations
on a small network of spiking neurons. We construct networks of spiking
neurons that simulate arbitrary threshold circuits, Turing machines,
and a certain type of random access machines with real valued inputs.
We also show that relatively weak basic assumptions about the response-
and threshold-functions of the spiking neurons are sufficient in order
to employ them for such computations.
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