Complexity of Boolean Computations for a Spiking Neuron
We investigate the computational power of a model for a spiking neuron
in the Boolean domain by comparing it with traditional neuron models such
as threshold gates (or McCulloch-Pitts neurons) and sigma-pi units (or
polynomial threshold gates). In particular, we estimate the number of gates
required to simulate a spiking neuron by a disjunction of threshold gates
and we establish tight bounds for this threshold number. Furthermore, we
analyze the degree of the polynomials that a sigma-pi unit must use when
simulating a spiking neuron. We show that this degree cannot be bounded
by any fixed value. Our results give evidence that the use of continuous
time as a computational resource endows single-cell models with substantially
larger computational capabilities.