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Neuromorphic hardware platforms, such as Intel's Loihi chip, support theimplementation of Spiking Neural Networks (SNNs) as an energy-efficientalternative to Artificial Neural Networks (ANNs). SNNs are networks of neuronswith internal analogue dynamics that communicate by means of binary timeseries. In this work, a probabilistic model is introduced for a generalizedset-up in which the synaptic time series can take values in an arbitraryalphabet and are characterized by both causal and instantaneous statisticaldependencies. The model, which can be considered as an extension of exponentialfamily harmoniums to time series, is introduced by means of a hybriddirected-undirected graphical representation. Furthermore, distributed learningrules are derived for Maximum Likelihood and Bayesian criteria under theassumption of fully observed time series in the training set.

Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing