We're about to simulate a human brain on a supercomputer The world's most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models will offer unprecedented insights into how our brains work What would it mean to simulate a human brain? Today's most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden. Researchers have long tried to isolate specific parts of the brain, modelling smaller regions with a computer to explain particular brain functions. But "we have never been able to bring them all together into one place, into one larger brain model where we can check whether these ideas are at all consistent", says Markus Diesmann at the Jülich Research Centre in Germany.

Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features. However, despite the potential relevance of SFA for modeling brain function, there is currently no SFA algorithm with a biologically plausible neural network implementation, by which we mean an algorithm operates in the online setting and can be mapped onto a neural network with local synaptic updates. In this work, starting from an SFA objective, we derive an SFA algorithm, called Bio-SFA, with a biologically plausible neural network implementation.

Learning to play a musical instrument can protect your brain from aging, building up a defense against cognitive decline that lasts a lifetime. Researchers from Canada and China discovered older adults who had spent years playing music were better at understanding speech in noisy environments, like a crowded room, compared to those who didn't play music. Their brains worked more like younger people's brains, needing less energy to focus than older non-musicians' brains had to use to make up for age-related mental declines. Playing music was found to build up a person's'cognitive reserve,' which is like a backup system in the brain. This reserve helps the brain stay efficient and work more like a younger brain, even as someone grows older.

Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features. However, despite the potential relevance of SFA for modeling brain function, there is currently no SFA algorithm with a biologically plausible neural network implementation, by which we mean an algorithm operates in the online setting and can be mapped onto a neural network with local synaptic updates. In this work, starting from an SFA objective, we derive an SFA algorithm, called Bio-SFA, with a biologically plausible neural network implementation.

Call the 988 Suicide and Crisis Lifeline or text TALK to 741741 at the Crisis Text Line if you are in need of help. Health experts say that artificial intelligence (AI) chatbots posing as therapists could cause "serious harm" to struggling people, including adolescents, without the proper safety measures. Christine Yu Moutier, M.D., Chief Medical Officer at the American Foundation for Suicide Prevention, told Fox News Digital there are "critical gaps" in research regarding the intended and unintended impacts of AI on suicide risk, mental health and larger human behavior. "The problem with these AI chatbots is that they were not designed with expertise on suicide risk and prevention baked into the algorithms. Additionally, there is no helpline available on the platform for users who may be at risk of a mental health condition or suicide, no training on how to use the tool if you are at risk, nor industry standards to regulate these technologies," Moutier said.

Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells in the primary visual cortex and hippocampus, suggesting that the brain uses temporal slowness as a computational principle for learning latent features. However, despite the potential relevance of SFA for modeling brain function, there is currently no SFA algorithm with a biologically plausible neural network implementation, by which we mean an algorithm operates in the online setting and can be mapped onto a neural network with local synaptic updates. In this work, starting from an SFA objective, we derive an SFA algorithm, called Bio-SFA, with a biologically plausible neural network implementation.

Earlier this month, facing an increasingly precarious situation, the US Food and Drug Administration (FDA) and the Drug Enforcement Administration (DEA) joined forces to address the ongoing Adderall shortage. Technically, neither organization has the power to compel pharmaceutical companies to produce mixed amphetamine salts, but in the face of skyrocketing diagnoses for attention deficit hyperactivity disorder (ADHD) in the pandemic era of telemedicine, they wanted to reassure the public that they were looking into potential alternatives to stimulant medications. In a joint statement, the agencies acknowledged that while they were actively working with the pharmaceutical industry to address the shortages, the FDA did approve a "game based digital therapeutic" to address ADHD symptoms in children back in 2020. While it's unclear whether digital therapeutics can replace stimulants entirely (they probably can't), it is clear that people want options beyond amphetamines. And this summer, digital medicine company Akili Interactive dropped the first "over-the-counter" digital therapeutic for managing ADHD symptoms in adults, using the same technology underlying their previously FDA-approved prescription video game for kids.

Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare. 1 Introduction Demystifying the principles that account for human intelligent behaviors, such as recognizing faces and making decisions, has been attracting a tremendous amount of interdisciplinary effort and is also the driving force behind the boom in artificial intelligence. The closer we can approach the intrinsicality of intelligence, the higher the possibility that we could master the emergence of intelligence. As the biological recesses of intelligent behaviors, the multiscale characteristics of the human brain are specifically being identified to explain the remarkable neurobiological basis underlying intelligent abilities.

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative mod- els for brain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our frame- work from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity mod- els which are derived from fMRI data are then validated through a group classification task.

Recently, to overcome the shallow nature of the linear models, various of deep learning based methods have been Using functional magnetic resonance imaging (fMRI) and proposed to discover the FBNs. Most of these methods are deep learning to explore functional brain networks (FBNs) based on the autoencoders, they use different autoencoders has attracted many researchers. However, most of these to extract the sources in an self-supervised manner, and then studies are still based on the temporal correlation between use the generative linear model, such as LASSO to generate the sources and voxel signals, and lack of researches on the the FBNs [6, 7]. In general, these deep learning based methods dynamics of brain function. Due to the widespread local can indeed extract better encoder representations as the correlations in the volumes, FBNs can be generated directly sources than the classical methods, such as ICA and SDL, but in the spatial domain in a self-supervised manner by using still generate FBNs in a linear and independent manner, with spatial-wise attention (SA), and the resulting FBNs has the sources extraction and the FBNs generation as 2 separate a higher spatial similarity with templates compared to the steps. Generating the FBNs in such way is time-consuming classical method. Therefore, we proposed a novel Spatial-and does not fully utilize the advantages of deep learning, and Temporal Convolutional Attention (STCA) model to discover cannot directly generate the FBNs with deep learning.