  We consider detection based on deep learning, and show it is possible to
train detectors that perform well without any knowledge of the underlying
channel models. Moreover, when the channel model is known, we demonstrate that
it is possible to train detectors that do not require channel state information
(CSI). In particular, a technique we call a sliding bidirectional recurrent
neural network (SBRNN) is proposed for detection where, after training, the
detector estimates the data in real-time as the signal stream arrives at the
receiver. We evaluate this algorithm, as well as other neural network (NN)
architectures, using the Poisson channel model, which is applicable to both
optical and molecular communication systems. In addition, we also evaluate the
performance of this detection method applied to data sent over a molecular
communication platform, where the channel model is difficult to model
analytically. We show that SBRNN is computationally efficient, and can perform
detection under various channel conditions without knowing the underlying
channel model. We also demonstrate that the bit error rate (BER) performance of
the proposed SBRNN detector is better than that of a Viterbi detector with
imperfect CSI as well as that of other NN detectors that have been previously
proposed. Finally, we show that the SBRNN can perform well in rapidly changing
channels, where the coherence time is on the order of a single symbol duration.
