We show that a simple statistical mechanics model can capture the collective behavior of large networks of spiking neurons. Qualitative arguments suggest that regularly firing neurons should be described by a planar "spin" of unit length. We extract these spins from spike trains and then measure the interaction Hamiltonian using simulations of small clusters of cells. Correlations among spike trains obtained from simulations of large arrays of cells are in quantitative agreement with the predictions from these Hamiltonians. We comment on the novel computational abilities of these "XY networks."

I demonstrate that it is possible to construct a three-layer network of the standard feedforward architecture that can solve the N input- N output encoder problem with just two hidden units.