Most artificially intelligent systems are based on neural networks, algorithms inspired by biological neurons found in the brain. These networks can consist of multiple layers, with inputs coming in one side and outputs going out of the other. The outputs can be used to make automatic decisions, for example, in driverless cars. Attacks to mislead a neural network can involve exploiting vulnerabilities in the input layers, but typically only the initial input layer is considered when engineering a defense. For the first time, researchers augmented a neural network’s inner layers with a process involving random noise to improve its resilience.
Clusters of abnormally folded α-synuclein proteins, called seeds, trigger the formation of larger aggregates, leading to neurodegenerative disorders known as synucleinopathies. Researchers from Japan have developed a novel assay that can detect α-synuclein seeds in the blood. This new assay is highly sensitive, simple, quick, and can differentiate between synucleinopathies based on structural differences of the amplified seeds. This research was supported by the National Research Program for Neurological Disorders and Mental Health of the Japan Agency for Medical Research and Development (AMED).
A team led by researchers from Tokyo Metropolitan University have identified the best methods to study the resting state of the brain in marmosets using functional MRI.
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