  Winner Take All (WTA) circuits a type of Spiking Neural Networks (SNN) have
been suggested as facilitating the brain's ability to process information in a
Bayesian manner. Research has shown that WTA circuits are capable of
approximating hierarchical Bayesian models via Expectation Maximization (EM).
So far, research in this direction has focused on bottom up processes. This is
contrary to neuroscientific evidence that shows that, besides bottom up
processes, top down processes too play a key role in information processing by
the human brain. Several functions ascribed to top down processes include
direction of attention, adjusting for expectations, facilitation of encoding
and recall of learned information, and imagery. This paper explores whether WTA
circuits are suitable for further integrating information represented in
separate WTA networks. Furthermore, it explores whether, and under what
circumstances, top down processes can improve WTA network performance with
respect to inference and learning. The results show that WTA circuits are
capable of integrating the probabilistic information represented by other WTA
networks, and that top down processes can improve a WTA network's inference and
learning performance. Notably, it is able to do this according to key
neuromorphic principles, making it ideal for low-latency and energy efficient
implementation on neuromorphic hardware.
