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NeuroCOLT
Technical Report NC-TR-01-102
2001-102
Complexity
of Learning for Networks of Spiking Neurons with Nonlinear Synaptic
Interactions
Michael Schmitt
ABSTRACT
We study model networks of spiking neurons where synaptic inputs
interact in terms of nonlinear functions. These nonlinearities are
used to represent the spatial grouping of synapses on the dendrites
and to model the computations performed at local branches. We analyze
the complexity of learning in these networks in terms of the VC dimension
and the pseudo dimension. Polynomial upper bounds on these dimensions
are derived for various types of synaptic nonlinearities.
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