Badong Chen

Abstract Major Depressive Disorder (MDD) is highly heterogeneous, limiting treatment efficacy. Despite efforts to delineate patient heterogeneity through subtyping, current approaches remain limited by noise, lack of clinical applicability, and insufficient external validation. Crucially, they focus on subtyping while neglecting staging information (e.g., illness duration). We developed BrainCVAE, a contrastive variational autoencoder, to disentangle MDD-specific neural features.

Abstract Neuromorphic electronics can provide in-memory computing systems with low power consumption by emulating key principles of the brain. However, their practical capabilities are limited by a number of challenges, including device non-ideality, limited training accuracy and insufficient adaptability. Here we explore the development of training approaches for neuromorphic electronics, including digital, mixed-signal and emerging neuromorphic electronics.