  A class of analog computers built from large numbers of microscopic
probabilistic machines is discussed. It is postulated that such computers are
implemented in biological systems as ensembles of protein molecules. The
formalism is based on an abstract computational model referred to as Protein
Molecule Machine (PMM). A PMM is a continuous-time first-order Markov system
with real input and output vectors, a finite set of discrete states, and the
input-dependent conditional probability densities of state transitions. The
output of a PMM is a function of its input and state. The components of input
vector, called generalized potentials, can be interpreted as membrane
potential, and concentrations of neurotransmitters. The components of output
vector, called generalized currents, can represent ion currents, and the flows
of second messengers. An Ensemble of PMMs (EPMM) is a set of independent
identical PMMs with the same input vector, and the output vector equal to the
sum of output vectors of individual PMMs. The paper suggests that biological
neurons have much more sophisticated computational resources than the presently
popular models of artificial neurons.
