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Scientists may be soon be able to grow replicas of human brains in a lab

Daily Mail - Science & tech

Scientists may soon be able to create human brains in a lab, according to the latest research. For the first time scientists have successfully grown a 3D model of the brain using human cells, allowing them to better study abnormal brain activity. Experts have been culturing brain tissue for years but this technique uses functional neutral tissue to create'brain-like organoids'. Researchers say in the future they could use cells from patients with Parkinson's and Alzheimer's to understand how they will respond to certain treatments. Scientists have successfully grown a 3D model of the brain from human neurons, providing them with a better opportunity to study abnormal brain cells.


Brain computer interfaces -- Why & why now? – Above Intelligent (AI)

#artificialintelligence

With the recent Facebook & Neuralink announcements, brain-computer interfaces are the big news this month. I wanted to explain this concept to my parents and kids in a quick summary, and then decided to write this post up here for comments. As I see it, there are broadly 2 main drivers for this. A few years ago, watching the movie Avatar with my son was a fascinating experience for its many novel concepts. Like genetically engineering a Na'vi body with the mind of a remotely located human to ultimately help colonize Pandora.


ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts

arXiv.org Machine Learning

fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of experts model for predicting brain activity patterns. Given a new stimulus, the model predicts the entire brain activation as a weighted linear combination of activation of multiple experts. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also helps in understanding which of the brain regions get activated together, given a certain kind of stimulus. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration.


Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

Neural Information Processing Systems

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer's disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to the alternation in the functional brain network, i.e., the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the "monotone property" we established in this paper. Our experimental results on neuroimaging PET data of 42 AD, 116 MCI, and 67 NC subjects reveal several interesting connectivity patterns consistent with literature findings, and also some new patterns that can help the knowledge discovery of AD.


Elon Musk's Neuralink may have tested interfaces on animals

Daily Mail - Science & tech

Elon Musk's Neuralink may be testing its'Matrix' style computer-brain interfaces on animals, it has emerged. Devices being developed by the firm are designed to give people advanced mental abilities, which Musk says will let humanity keep up with future'god-like' AI systems. City planning documents submitted by the company reveal plans for'a small operating room for in vivo testing, and a small room to house rodents.' It is not known what the tests involved, whether they actually took place, or if they are on-going, as Neuralink has refused to comment on the matter. Elon Musk's (pictured) Neuralink may be testing its'Matrix' style computer-brain interfaces on animals, it has emerged.