Neuron
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Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a learning algorithm, such as network architecture and learning rule. ⋯ Brains, on the other hand, generalize across comparatively drastic changes in the sensory input all the time. We highlight some shortcomings of state-of-the-art learning algorithms compared to biological brains and discuss several ideas about how neuroscience can guide the quest for better inductive biases by providing useful constraints on representations and network architecture.
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Pain is a subjective sensory experience that can, mostly, be reported but cannot be directly measured or quantified. Nevertheless, a suite of biomarkers related to mechanisms, neural activity, and susceptibility offer the possibility-especially when used in combination-to produce objective pain-related indicators with the specificity and sensitivity required for diagnosis and for evaluation of risk of developing pain and of analgesic efficacy. Such composite biomarkers will also provide improved understanding of pain pathophysiology.
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The nature of fluid dynamics within the brain parenchyma is a focus of intensive research. Of particular relevance is its participation in diseases associated with protein accumulation and aggregation in the brain, such as Alzheimer's disease (AD). ⋯ Given that meningeal lymphatic vessels are functionally linked to paravascular influx/efflux of the CSF/ISF, and in light of recent findings that certain cytokines, classically perceived as immune molecules, exert neuromodulatory effects, it is reasonable to suggest that the activity of meningeal lymphatics could alter the accessibility of CSF-borne immune neuromodulators to the brain parenchyma, thereby altering their effects on the brain. Accordingly, in this Perspective we propose that the meningeal lymphatic system can be viewed as a novel player in neurophysiology.
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While chronic pain is considered by some to be a CNS disease, little is understood about underlying neurobiological mechanisms. Addiction models have heuristic value in this regard, because both pain and addictive disorders are characterized by impaired hedonic capacity, compulsive drug seeking, and high stress. In drug addiction such symptomatology has been attributed to reward deficiency, impaired inhibitory control, incentive sensitization, aberrant learning, and anti-reward allostatic neuroadaptations. Here we propose that similar neuroadaptations exist in chronic pain patients.
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Recent neuroimaging studies suggest that the brain adapts with pain, as well as imparts risk for developing chronic pain. Within this context, we revisit the concepts for nociception, acute and chronic pain, and negative moods relative to behavior selection. ⋯ We deconstruct chronic pain into four distinct phases, each with specific mechanisms, and outline current state of knowledge regarding these mechanisms: the limbic brain imparting risk, and the mesolimbic learning processes reorganizing the neocortex into a chronic pain state. Moreover, pain and negative moods are envisioned as a continuum of aversive behavioral learning, which enhance survival by protecting against threats.