  In order to fully exploit the potential of molecular communication (MC) for
intra-body communication, practically implementable cellular receivers are an
important long-term goal. A variety of receiver architectures based on chemical
reaction networks (CRNs) and gene-regulatory networks (GRNs) has been
introduced in the literature, because cells use these concepts to perform
computations in nature. However, practical feasibility is still limited by
stochastic fluctuations of chemical reactions and long computation times in
GRNs. Therefore, in this paper, we propose two receiver designs based on
stochastic CRNs, i.e., CRNs that perform computations by exploiting the
intrinsic fluctuations of chemical reactions with very low molecule counts. The
first CRN builds on a recent result from chemistry that showed how Boltzmann
machines (BMs), a commonly used machine learning model, can be implemented with
CRNs. We show that BMs with optimal parameter values and their CRN
implementations can act as maximum-a-posteriori (MAP) detectors. Furthermore,
we show that BMs can be efficiently trained from simulation data to achieve
close-to-MAP performance. While this approach yields a fixed CRN once deployed,
our second approach based on a manually designed CRN can be trained with pilot
symbols even within the cell and thus adapt to changing channel conditions. We
extend the literature by showing that practical robust detectors can achieve
close-to-MAP performance even without explicit channel knowledge.
