Brain disorders such as Alzheimer's disease, epilepsy, Parkinson's disease have attracted significant research interest due to their profound impact on patients' quality of life and healthcare systems globally [1, 2]. Timely and accurate diagnosis is crucial for effective intervention and management, necessitating reliable tools capable of capturing the dynamic changes in brain activity. Electroencephalography (EEG), a cost-effective and non-invasive method for real-time monitoring of brain function, has become a cornerstone in clinical practice for detecting brain disorders. By measuring electrical activity in the brain, EEG provides valuable insights into neural dynamics, particularly for conditions like epilepsy and Alzheimer's disease, where the identification of abnormal patterns is critical for diagnosis and treatment. Recent advances in deep learning (DL) have significantly enhanced the capabilities of computer-aided diagnosis (CAD) systems for EEG analysis. These systems excel at extracting complex, high-dimensional features from raw EEG signals, improving diagnostic accuracy across various applications [3, 4, 5].

Facebook founder Mark Zuckerberg reportedly loves conducting meetings while walking, and so did Apple founder Steve Jobs - and scientists have shown that they were right on target. Just 20 minutes of walking can prepare the brain to take in and retain new information, neuroscience research has shown. These positive effects can be seen in areas of the brain involved in making decisions, managing stress, and planning our behavior. Other forms of exercise have their own benefits on brain health, too, but this research determined that it doesn't take much to boost your brain power - and a little bit of walking is much better than no exercise at all. Just 20 minutes of walking can prepare the brain to take in and retain new information, neuroscience research has shown.

The BRAIN-AI initiative, a fusion of artificial intelligence and neuroscience research by the JST ERATO Ikegaya Brain-AI Fusion Project, has the potential to break through existing limitations on brain activity and expand human capabilities. Meanwhile, during the BRAIN-AI initiative, experts are expected to examine problems on ethical, legal, and social aspects, that must be discussed. These problems include the information handling in the human brain itself (the most private information available), the potential for increased inequality due to future technologies that enhance human capabilities, and philosophical issues surrounding identities such as who is responsible for decisions made by a brain–computer interface (BCI). To address these challenges, the BRAIN–AI HITE collaboration effort brings together BRAIN–AI researchers with researchers specializing in ethical, legal, and social implications (ELSI); responsible research and innovation (RRI); and philosophy. The goal is to ensure that BRAIN–AI research can bring true happiness (well-being) to humans by examining, from the earliest stages of research, the impact of this technology on people and society based on the in-depth knowledge of humanities and social sciences. As part of this effort, a team led by Associate Professor Ryuma Shineha and Specially Appointed Assistant Professor Shu Ishida at Osaka University's Research Center on Ethical, Legal, and Social Issues (ELSI) conducted a survey on attitudes toward brain science and brain information, targeting the general public (2,000 responses) and researchers in the field of neuroscience (108 responses).

This implies that the bulk of the work in developing general AI can be achieved by building systems that match the perceptual and motor abilities of animals and that the subsequent step to human-level intelligence would be considerably smaller. This is good news because progress on the first goal can rely on the favored subjects of neuroscience research - rats, mice, and non-human primates - for which extensive and rapidly expanding behavioral and neural datasets can guide the way. Thus, we believe that the NeuroAI path will lead to necessary advances if we figure out the core capabilities that all animals possess in embodied sensorimotor interaction with the world. NeuroAI Grand Challenge: The Embodied Turing Test In 1950, Alan Turing proposed the "imitation game" as a test of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human

The study of artificial intelligence (AI) and neuroscience have many things in common. At its core, neurosciences aim to better understand the brain by deciphering its complex networks and processes. Complimentarily, many AI-focused research projects involve constructing synthetic components of the human brain. The connection of these fields benefits both computer scientists and biology-focused neuroscientists as they help us understand natural and artificial learning systems. These domains of research lend themselves to be inspired by one another.

I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. The brain has always been considered the main inspiration for the field of artificial intelligence(AI). For many AI researchers, the ultimate goal of AI is to emulate the capabilities of the brain.

As a basis for modelling brain function, deep learning has in recent years been used to model systems in vision, audition, motor control, navigation, and cognitive control. In a new paper, DeepMind researchers call attention to another "fundamentally novel" development in AI research -- deep reinforcement learning (deep RL) -- which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists. The first neuroscience applications of supervised deep learning can be traced back to the 1980s. The increasing availability of more powerful computers over the past decade has renewed research efforts in applying AI approaches -- especially supervised deep learning -- to neuroscience. Deep RL unites deep learning and reinforcement learning, a computational framework that has already had a substantial impact on neuroscience research.

As most scientists will tell you, we are still decades away from building artificial general intelligence, machines that can solve problems as efficiently as humans. On the path to creating general AI, the human brain, arguably the most complex creation of nature, is the best guide we have. Advances in neuroscience, the study of nervous systems, provide interesting insights into how the brain works, a key component for developing better AI systems. Reciprocally, the development of better AI systems can help drive neuroscience forward and further unlock the secrets of the brain. For instance, convolutional neural networks (CNN), one of the key contributors to recent advances in artificial intelligence, are largely inspired by neuroscience research on the visual cortex.

The brain has always been considered the main inspiration for the field of artificial intelligence(AI). For many AI researchers, the ultimate goal of AI is to emulate the capabilities of the brain. That seems like a nice statement but its an incredibly daunting task considering that neuroscientist are still struggling trying to understand the cognitive mechanism that power the magic of our brains. Despite the challenges, more regularly we are seeing AI research and implementation algorithms that are inspired by specific cognition mechanisms in the human brain and that have been producing incredibly promising results. Recently, the DeepMind team published a paper about neuroscience-inspired AI that summarizes the circle of influence between AI and neuroscience research.

Reports from some of these first projects make up the majority of the book, with the balance of the book providing an overview of neuroinformatics. The book's foreword provides interesting history and perspective on the incubation of neuroinformatics. The preface and first two chapters of the book explain neuroinformatics and the motivation for it. As with so many other fields, there has been an information explosion in neuroscience research. Data are produced by tens of thousands of investigators in hundreds of journals.