  If edge devices are to be deployed to critical applications where their
decisions could have serious financial, political, or public-health
consequences, they will need a way to signal when they are not sure how to
react to their environment. For instance, a lost delivery drone could make its
way back to a distribution center or contact the client if it is confused about
how exactly to make its delivery, rather than taking the action which is "most
likely" correct. This issue is compounded for health care or military
applications. However, the brain-realistic temporal credit assignment problem
neuromorphic computing algorithms have to solve is difficult. The double role
weights play in backpropagation-based-learning, dictating how the network
reacts to both input and feedback, needs to be decoupled. e-prop 1 is a
promising learning algorithm that tackles this with Broadcast Alignment (a
technique where network weights are replaced with random weights during
feedback) and accumulated local information. We investigate under what
conditions the Bayesian loss term can be expressed in a similar fashion,
proposing an algorithm that can be computed with only local information as well
and which is thus no more difficult to implement on hardware. This algorithm is
exhibited on a store-recall problem, which suggests that it can learn good
uncertainty on decisions to be made over time.
