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Abstract Risk stratification is an important tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for training any neural network architecture on any data modality to identify prognostically distinct patient groups by directly optimizing for survival heterogeneity across patient clusters.
Neuroscientific investigations of the neural mechanisms underlying human behavior frequently use artificial laboratory tasks to optimize experimental control. However, adopting naturalistic tasks and paradigms may provide additional mechanistic insight and even translational benefit. For example, in visual neuroscience, shifting from artificial toward more naturalistic stimuli has improved artificial visual perception (1).
Highlights • Establishment of patch-clamp recordings from boutons in the neocortex • High-resolution current clamp recordings reveal large and rapid action potentials • Action potentials reliably propagate into axon collaterals in the neocortex • Action potentials show differential plasticity in excitatory and inhibitory boutons Summary Presynaptic action potential spikes control neurotransmitter release and thus interneuronal communication.
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