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.

翻译：神经计算领域的最新进展已采用胜者全取（WTA）电路，以促进层级贝叶斯推理与脉冲神经网络在神经生物学合理的信息处理模型框架下的统一。当前研究通常通过分类任务（尤其是MNIST数据集）验证这些网络的性能。然而，如何将这类网络的随机响应转化为离散决策——即群体解码过程——尚未达成共识。尽管群体解码作为脉冲神经网络中常被忽视的环节，本研究表明其对WTA网络的分类性能具有显著影响。为此，我们将WTA网络应用于基于多组学数据的癌症亚型诊断问题，采用来自癌症基因组图谱（TCGA）的数据集。在此过程中，我们创新性地实现了基因相似性网络——一种基于Kohonen自组织映射算法的特征编码技术。此外，我们进一步证明，在面临不平衡数据集时，选择特定群体解码方法的影响会显著放大。