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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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