  Artificial neural networks built from two-state neurons are powerful
computational substrates, whose computational ability is well understood by
analogy with statistical mechanics. In this work, we introduce similar
analogies in the context of spiking neurons in a fixed time window, where
excitatory and inhibitory inputs drawn from a Poisson distribution play the
role of temperature. For single neurons with a "bandgap" between their inputs
and the spike threshold, this temperature allows for stochastic spiking. By
imposing a global inhibitory rhythm over the fixed time windows, we connect
neurons into a network that exhibits synchronous, clock-like updating akin to
neural networks. We implement a single-layer Boltzmann machine without learning
to demonstrate our model.
