What is the Best Way to Understand Consciousness?

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The classical view of perception is that the brain processes sensory information in a bottom-up or'outside-in' direction: sensory signals enter through receptors (for example, the retina) and then progress deeper into the brain, with each stage recruiting increasingly sophisticated and abstract processing. In this view, the perceptual'heavy-lifting' is done by these bottom-up connections. The Helmholtzian view inverts this framework, proposing that signals flowing into the brain from the outside world convey only prediction errors – the differences between what the brain expects and what it receives. Perceptual content is carried by perceptual predictions flowing in the opposite (top-down) direction, from deep inside the brain out towards the sensory surfaces. Perception involves the minimisation of prediction error simultaneously across many levels of processing within the brain's sensory systems, by continuously updating the brain's predictions.


A Wearable Chip to Predict Seizures

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One of the toughest aspects of having epilepsy is not knowing when the next seizure will strike. A wearable warning system that detects pre-seizure brain activity and alerts people of its onset could alleviate some of that stress and make the disorder more manageable. To that end, IBM researchers say they have developed a portable chip that can do the job; they described their invention today in the Lancet's open access journal eBioMedicine.


Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

arXiv.org Machine Learning

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual's search intent was modeled and successfully used for retrieving new, relevant documents from the whole English Wikipedia corpus. The results show that the users' interests towards digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction, and may be applied across diverse information-intensive applications.


Getting Started in the Seizure Prediction Competition: Impact, History, & Useful Resources

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The currently ongoing Seizure Prediction competition--hosted by Melbourne University AES, MathWorks, and NIH--invites Kagglers to accurately forecast the occurrence of seizures using intracranial EEG recordings. This competition uniquely focuses on seizure prediction using long-term electrical brain activity from human patients obtained from the world first clinical trial of the implantable NeuroVista Seizure Advisory Sytem. In this blog post, you'll learn about the contest's potential to positively impact the lives of those who suffer from epilepsy, outcomes of previous seizure prediction contests on Kaggle, as well as resources which will help you get started in the competition including a free temporary MATLAB license and starter code. This competition is sponsored by MathWorks, the National Institutes of Health (NINDS), the American Epilepsy Society and the University of Melbourne, and organised in partnership with the Alliance for Epilepsy Research, the University of Pennsylvania and the Mayo Clinic. For many people with epilepsy, seizures reoccur at random times and greatly disrupt their cognitive and emotional state, their ability to work and drive, and their social and economic situation.


Why did humans evolve big brains? We don't know, but math can help

PBS NewsHour

Our brains have a finite capacity for processing information and for remembering, and the bigger the brain, the more oxygen and sugar it takes to maintain. Math may solve why people are such eggheads. A new model published Thursday in PLOS Computational Biology mathematically illustrates what led to the evolution of humans' abnormally large brains. Evolutionary biologists devised these equations to tease apart the relationship between human brain size and the cost of maintaining a large brain. Over the last few decades, the pace and stages of brain growth in humans have become clearer.