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.

翻译：胜者全取（WTA）电路是一种脉冲神经网络（SNN），被认为有助于大脑以贝叶斯方式处理信息。研究表明，WTA电路能够通过期望最大化（EM）算法近似层次化贝叶斯模型。然而，目前该方向的研究主要集中在自下而上的过程中。这与神经科学证据相悖——该证据显示，除了自下而上过程外，自上而下过程在人类大脑信息处理中也起着关键作用。自上而下过程的功能包括注意力引导、期望调整、促进学习信息的编码与回忆、以及想象。本文探讨了WTA电路是否适用于进一步整合不同WTA网络中表征的信息。此外，本文还研究了自上而下过程能否以及在何种条件下提升WTA网络在推理和学习上的性能。结果表明，WTA电路能够整合其他WTA网络所表征的概率信息，并且自上而下过程可以提升WTA网络的推理与学习性能。值得注意的是，这一过程符合关键的神经形态计算原则，使其非常适合在神经形态硬件上实现低延迟和高能效的运行。