3 papers
cs.NE2024
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 spiki…
cs.LG2023
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data
Mark Peelen, Leila Bagheriye, Johan Kwisthout
The integration of multi-omics data has emerged as a promising approach for gaining comprehensive insights into complex diseases such as cancer. This paper proposes a novel approac…
cs.AI2023
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
Otto van der Himst, Leila Bagheriye, Johan Kwisthout
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…