  Among the main features of biological intelligence are energy efficiency,
capacity for continual adaptation, and risk management via uncertainty
quantification. Neuromorphic engineering has been thus far mostly driven by the
goal of implementing energy-efficient machines that take inspiration from the
time-based computing paradigm of biological brains. In this paper, we take
steps towards the design of neuromorphic systems that are capable of adaptation
to changing learning tasks, while producing well-calibrated uncertainty
quantification estimates. To this end, we derive online learning rules for
spiking neural networks (SNNs) within a Bayesian continual learning framework.
In it, each synaptic weight is represented by parameters that quantify the
current epistemic uncertainty resulting from prior knowledge and observed data.
The proposed online rules update the distribution parameters in a streaming
fashion as data are observed. We instantiate the proposed approach for both
real-valued and binary synaptic weights. Experimental results using Intel's
Lava platform show the merits of Bayesian over frequentist learning in terms of
capacity for adaptation and uncertainty quantification.
