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.

本文研究了 Winner Take All（WTA）回路在整合分布于不同 WTA 网络中的信息以及通过自上而下的过程改进 WTA 网络推理和学习性能方面的适用性，并确认其可根据关键的神经形态学原理实现低延迟和高能效的神经形态硬件。