New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Elon Musk's Neuralink rival Synchron has begun human trials of its brain implant that lets the wearer control a computer using thought alone. The firm's Stentrode brain implant, about the size of a paperclip, will be implanted in six patients in New York and Pittsburgh who have severe paralysis. Stentrode will let patients control digital devices just by thinking and give them back the ability to perform daily tasks, including texting, emailing and shopping online. Although the implant has already been implanted and tested in Australian patients, the new clinical trial marks the first time it will be tested in the US. If successful, the Stentrode brain implant could be sold as a commercial product aimed at paralysis patients to regain their independence and quality of life.
ANJA KASPERSEN: Today's podcast will focus on artificial intelligence (AI), neuroscience, and neurotechnologies. My guest today is Ricardo Chavarriaga. Ricardo is an electrical engineer and a doctor of computational neuroscience. He is currently the head of the Swiss office of the Confederation of Laboratories for AI Research in Europe (CLAIRE) and a senior researcher at Zurich University of Applied Sciences. Ricardo, it is an honor and a delight to share the virtual stage with you today. I am really happy and looking forward to a nice discussion today. ANJA KASPERSEN: Neuroscience is a vast and fast-developing field. Maybe you could start by providing our listeners with some background. When we think about the brain, this is something that has fascinated humanity for a long time. The question of how this organ that we have inside our heads can rule our behavior and can store and develop knowledge has been indeed one of the questions for science for many, many years. Neurotechnologies, computational neuroscience, and brain-machine interfaces are tools that we have developed to approach the understanding of this fabulous organ. When we talk about computational neuroscience it is the use of computational tools to create models of the brain. It can be mathematical models, it can be algorithms that try to reproduce our observations about the brain. It can be experiments on humans and on animals: these experiments can be behavioral, they can involve measurements of brain activity, and by looking at how the brains of organisms react and how the activity changes we will then try to apply our knowledge to create models for that. These models can have different flavors. We can for instance have very detailed models of electrochemical processes inside a neuron, and then we are looking at just a small part of the brain. We can have large-scale models with fewer details of how different brain structures interact among themselves, or even less-detailed models that try to reproduce behavior that we observe in animals and in humans as a result of certain mental disorders. We can even test these models using probes to tap into how can our brain construct representations of the world based on images, based on tactile, and based on auditory information.
Scientists have discovered a brain circuit that boosts maths skills in children and could even be targeted to improve learning. The circuit triggers an area near the back of the head known as the IPS (intraparietal sulcus), which is involved in processing figures, and is linked to the hippocampus where memories are stored. Before children can learn to add and subtract, they must learn which abstract symbol, like '4' or '6', represents which quantity, a skill also known as'number sense'. Experts know the IPS plays a role in number processing but the circuits involved in learning number sense had remained a mystery until now. Lead author Dr Hyesang Chang, of Stanford University, California, said: 'Mathematical skill development relies on number sense, the ability to discriminate between quantities.
When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition -- just like the human brain. The human brain seems to care a lot about faces. It's dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency -- and neuroscientists at MIT's McGovern Institute for Brain Research have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out. The finding, reported on March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.
The human brain seems to care a lot about faces. It's dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency -- and neuroscientists at MIT's McGovern Institute for Brain Research have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out. The finding, reported March 16 in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition. "The human brain's solution is to segregate the processing of faces from the processing of objects," explains Katharina Dobs, who led the study as a postdoc in the lab of McGovern investigator Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT.
Entrepreneur Bryan Johnson says he wanted to become very rich in order to do something great for humankind. Last year Johnson, founder of the online payments company Braintree, starting making news when he threw $100 million behind Kernel, a startup he founded to enhance human intelligence by developing brain implants capable of linking people's thoughts to computers. Johnson isn't alone in believing that "neurotechnology" could be the next big thing. To many in Silicon Valley, the brain looks like an unconquered frontier whose importance dwarfs any achievement made in computing or the Web. According to neuroscientists, several figures from the tech sector are currently scouring labs across the U.S. for technology that might fuse human and artificial intelligence.
Snap has bought NextMind, a French startup developing brain-computer interface technology (BCI) to help steer wearables and other devices by focusing on virtual buttons. There's no mystery about the intentions -- NextMind will aid Snap's augmented reality development, including work on Spectacles. Snap didn't disclose the value of the deal or outline its exact plans. NextMind will remain in its hometown of Paris while helping the Snap Lab team, although The Verge learned the newly-acquired company will discontinue its BCI headband for developers. The purchase isn't surprising given Snap's history. It bought WaveOptics, the company behind Spectacles' AR displays, in 2021.
The Olympics are all about emotion – the drama of world-class competition, the pageantry of medal ceremonies, and the moment-to-moment celebrations of the human spirit in action. The 2022 Winter Games kicked off on February 4th in Beijing, China. Despite the fact that the Games feel a little different because of COVID restrictions, nearly 3,000 athletes from 91 countries are competing in 109 events across events like alpine skiing, figure skating, ice hockey, luge, bobsled, snowboarding, and speed skating. And behind the scenes, there are huge technological advances helping athletes become better, faster, and stronger. Let's take a look at how artificial intelligence, the IoT, and intelligent devices are being used at the Olympic Games.
The outer layer of the cerebral hemisphere is termed the cerebral cortex. This is inter-connected via pathways that run sub-cortically. It is these connections as well as the connections from the cerebral cortex to the brainstem, spinal cord and nuclei deep within the cerebral hemisphere that form the white matter of the cerebral hemisphere. The deep nuclei include structures such as the basal ganglia and the thalamus. The main difference between cerebrum and cerebral cortex is that cerebrum is the largest part of the brain whereas cerebral cortex is the outer layer of the cerebrum.
Elon Musk's brain-chip firm Neuralink has admitted monkeys died during tests, but denied claims of animal abuse put forward by an animal rights group. The biotech firm is developing a brain-computer interface, that it claims could one day make humans hyper-intelligent, and allow paralyzed people to walk again. Last week the Physicians Committee for Responsible Medicine (PCRM) lodged a complaint with the US Department of Agriculture, alleging several counts of animal abuse between 2017 and 2020, involving test monkeys owned by Neuralink. They claimed the macaque monkeys, housed at a University of California Davis research facility, were subject to experiments that amounted to torture, with evidence of rashes, self-mutilation and brain hemorrhages seen in documentation. Neuralink has hit back at the claims of abuse, calling out the PCRM as a group that oppose any use of animals in research.