  Spiking Neural Networks (SNNs) offer a novel computational paradigm that
captures some of the efficiency of biological brains by processing through
binary neural dynamic activations. Probabilistic SNN models are typically
trained to maximize the likelihood of the desired outputs by using unbiased
estimates of the log-likelihood gradients. While prior work used single-sample
estimators obtained from a single run of the network, this paper proposes to
leverage multiple compartments that sample independent spiking signals while
sharing synaptic weights. The key idea is to use these signals to obtain more
accurate statistical estimates of the log-likelihood training criterion, as
well as of its gradient. The approach is based on generalized
expectation-maximization (GEM), which optimizes a tighter approximation of the
log-likelihood using importance sampling. The derived online learning algorithm
implements a three-factor rule with global per-compartment learning signals.
Experimental results on a classification task on the neuromorphic MNIST-DVS
data set demonstrate significant improvements in terms of log-likelihood,
accuracy, and calibration when increasing the number of compartments used for
training and inference.
