Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspective.

Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In cognitive neuroscience, graph modeling is a powerful framework widely used to study brain structural and functional connectivity. Yet, the extension of graph modeling to ANNs has been poorly explored especially in terms of functional connectivity (i.

Graph-based methods coupled with specific distributional distances for adversarial attack detection.

Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These adversarial attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them.

Derivation and study of human epithelial cell lines resistant to killing by chromium trioxide.

CrO3 is cytotoxic for human epithelial 293 kidney cells over a narrow concentration range of approximately 2-8 microM (D50 approximately 3.0 microM); significantly greater toxicity is observed in clonogenic assays (D50 approximately 0.1-1.