Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer
  Subtype Diagnosis
Charles Theodore Kent Leila Bagheriye Johan Kwisthout
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.