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ANNALS SPECIAL EDITION: THERAPEUTIC PROSPECTS New Innovations: Therapeutic Opportunities for Intellectual Disabilities
"... Intellectual disability is common and is associated with significant morbidity. Until the latter half of the 20th century, there were no efficacious treatments. Following initial breakthroughs associated with newborn screening and meta-bolic corrections, little progress was made until recently. With ..."
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Intellectual disability is common and is associated with significant morbidity. Until the latter half of the 20th century, there were no efficacious treatments. Following initial breakthroughs associated with newborn screening and meta-bolic corrections, little progress was made until recently. With improved understanding of genetic and cellular mech-anisms, novel treatment options are beginning to appear for a number of specific conditions. Fragile X and tuberous sclerosis offer paradigms for the development of targeted therapeutics, but advances in understanding of other dis-orders such as Down syndrome and Rett syndrome, for example, are also resulting in promising treatment directions. In addition, better understanding of the underlying neurobiology is leading to novel developments in enzyme replacement for storage disorders and adjunctive therapies for metabolic disorders, as well as potentially more gen-eralizable approaches that target dysfunctional cell regulation via RNA and chromatin. Physiologic therapies, includ-ing deep brain stimulation and transcranial magnetic stimulation, offer yet another direction to enhance cognitive functioning. Current options and evolving opportunities for the intellectually disabled are reviewed and exemplified.
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"... Abstract—Conspiracy theories, or in general seriously dis-torted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than sci-ence. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotio ..."
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Abstract—Conspiracy theories, or in general seriously dis-torted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than sci-ence. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal character-istics of the incoming information and the plasticity of the net-work, memory may self-organize creating memes with large attractor basins linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins distorting input pattern representations in associative memory. Such system-level mod-els may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics. I.
Short Title: Learning grid cells and place cells
"... † Authorship in alphabetical order ..."
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ANOTHER ADAPTIVE APPROACH TO NOVELTY DETECTION IN TIME SERIES
"... This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error ..."
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This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal.
