paper announcement

[Barak Pearlmutter]

The following paper, to appear in Neural Computation, is available via ftp
to archive.cis.ohio-state.edu:/pub/neuroprose/pearlmutter.vcdifn.ps.Z.


	  VC Dimension of an Integrate-and-Fire Neuron Model


	   Anthony M. Zador            Barak A. Pearlmutter
	    Salk Institute          Siemens Corporate Research
      10010 N. Torrey Pines Rd.       755 College Road East
	 La Jolla, CA  92037           Princeton, NJ  08540
	    zador at salk.edu             bap at scr.siemens.com


			       ABSTRACT

We compute the VC dimension of a leaky integrate-and-fire neuron
model.  The VC dimension quantifies the ability of a function class to
partition an input pattern space, and can be considered a measure of
computational capacity.  In this case, the function class is the class
of integrate-and-fire models generated by varying the integration time
constant and the threshold, the input space they partition is the
space of continuous-time signals, and the binary partition is
specified by whether or not the model reaches threshold at some
specified time.  We show that the VC dimension diverges only
logarithmically with the input signal bandwidth.  We also extend this
approach to arbitrary passive dendritic trees.  The main contributions
of this work are (1) it offers a formal treatment of the computational
capacity of a dynamical system; and (2) it provides a framework for
analyzing the computational capabilities of the dynamical systems
defined by networks of real neurons.

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Thanks for Jordan Pollack for maintaining the neuroprose archive.