Recent strides in the field of neural computation has seen the adoption of
Winner Take All (WTA) circuits to facilitate the unification of hierarchical
Bayesian inference and spiking neural networks as a neurobiologically plausible
model of information processing. Current research commonly validates the
performance of these networks via classification tasks, particularly of the
MNIST dataset. However, researchers have not yet reached consensus about how
best to translate the stochastic responses from these networks into discrete
decisions, a process known as population decoding. Despite being an often
underexamined part of SNNs, in this work we show that population decoding has a
significanct impact on the classification performance of WTA networks. For this
purpose, we apply a WTA network to the problem of cancer subtype diagnosis from
multi omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing
so we utilise a novel implementation of gene similarity networks, a feature
encoding technique based on Kohoens self organising map algorithm. We further
show that the impact of selecting certain population decoding methods is
amplified when facing imbalanced datasets.

近期神经计算领域的研究取得了长足的进展，在神经元计算和脉冲神经网络的结合中采用了 Winner Take All (WTA) 电路作为一个符合神经生物学可行性的信息处理模型。然而，关于如何将这些网络的随机响应最佳转化为离散决策的过程，即人口解码，研究人员尚未达成共识。我们的研究表明，人口解码对 WTA 网络的分类性能有重要影响。为了验证这一点，我们将 WTA 网络应用于使用来自癌症基因组图谱 (TCGA) 数据集的多组学数据进行癌症亚型诊断问题，并使用基于 Kohoens 自组织映射算法的基因相似性网络的新型特征编码技术。我们进一步表明，在面对不平衡数据集时，选择特定的人口解码方法会放大影响。

神经群体解码与癌症亚型诊断不平衡多组学数据集

Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer  Subtype Diagnosis

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。