Head injury models are important tools to study concussion biomechanics but are
impractical for real-world use because they are too slow. Here, we develop a convolutional …

Cell cycle reentry followed by neuronal hyperploidy and synaptic failure are two early
hallmarks of Alzheimer's disease (AD), however their functional connection remains …

Deep neural networks, inspired by information processing in the brain, can achieve human-
like performance for various tasks. However, research efforts to use these networks as …

The diagnoses of medical systems are widely applied for fast identification diseases,
remedial, and risk-free treatment for indented diseases. It is also growing technology; even …

The aim of this work was to enhance the biological feasibility of a deep convolutional neural
network-based in-silico model of neurodegeneration of the visual system by equipping it …

When subjected to stress, terminally differentiated neurons are susceptible to reactivate the
cell cycle and become hyperploid. This process is well documented in Alzheimer's disease …

Computational modeling of the neural activity in the human spinal cord may help elucidate
the underlying mechanisms involved in the complex processing of painful stimuli. In this …

Complex random networks provide a powerful mathematical framework to study high-
dimensional physical and biological systems. Several features of network structure (eg …

Recent investigations of traumatic brain injuries have shown that these injuries can result in
conformational changes at the level of individual neurons in the cerebral cortex. Focal …

Most organisms suffer neuronal damage throughout their lives, which can impair
performance of core behaviors. Their neural circuits need to maintain function despite injury …