Scientists just taught hundreds of thousands of neurons in a dish to play Pong. Using a series of strategically timed and placed electrical zaps, the neurons not only learned the game in a virtual environment, but played better over time\u2014with longer rallies and fewer misses\u2014showing a level of adaptation previously thought impossible.<\/p>\n\n\n\n
Why? Picture literally taking a chunk of brain tissue, digesting it down to individual neurons and other brain cells, dumping them (gently) onto a plate, and now being able to teach them, outside a living host, to respond and adapt to a new task using electrical zaps alone.<\/p>\n\n\n\n
It\u2019s not just fun and games. The biological neural network joins its artificial cousin, DeepMind\u2019s deep learning algorithms, in a growing pantheon of attempts at deconstructing, reconstructing, and one day mastering a sort of general \u201cintelligence\u201d based on the human brain.<\/p>\n\n\n\n
The brainchild of Australian company Cortical Labs<\/a>, the entire setup, dubbed DishBrain<\/em>, is the \u201cfirst real-time synthetic biological intelligence platform,\u201d according to the authors of a paper published<\/a> this month in Neuron<\/em>. The setup, smaller than a dessert plate, is extremely sleek. It hooks up isolated neurons with chips that can both record the cells\u2019 electrical activity and trigger precise zaps to alter those activities. Similar to brain-machine interfaces, the chips are controlled with sophisticated computer programs, without any human input.<\/p>\n\n\n\n The chips act as a bridge for neurons to link to a virtual world. As a translator for neural activity, they can unite biological electrical data with silicon bits, allowing neurons to respond to a digital game world.<\/p>\n\n\n\n DishBrain <\/em>is set up to expand to further games and tests. Because the neurons can sense and adapt to the environment and output their results to a computer, they could be used as part of drug screening tests. They could also help neuroscientists better decipher how the brain organizes its activity and learns, and inspire new machine learning methods.<\/p>\n\n\n\n But the ultimate goal, explained Dr. Brett Kagan, chief scientific officer at Cortical Labs, is to help harness the inherent intelligence of living neurons for their superior computing power and low energy consumption. In other words, compared to neuromorphic hardware that mimics neural computation, why not just use the real thing?<\/p>\n\n\n\n \u201cTheoretically, generalized SBI [synthetic biological intelligence] may arrive before artificial general intelligence (AGI) due to the inherent efficiency and evolutionary advantage of biological systems,\u201d the authors wrote in their paper.<\/p>\n\n\n\n The DishBrain<\/em> project started with a simple idea: neurons are incredibly intelligent and adaptable computing machines<\/a>. Recent studies suggest that each neuron is a supercomputer in itself, with branches once thought passive acting as independent mini-computers. Like people within a community, neurons also have an inherent ability to hook up to diverse neural networks, which dynamically shifts with their environment.<\/p>\n\n\n\n This level of parallel, low-energy computation has long been the inspiration for neuromorphic chips and machine learning algorithms<\/a> to mimic the natural abilities of the brain. While both have made strides, none have been able to recreate the complexity of a biological neural network.<\/p>\n\n\n\n \u201cFrom worms to flies to humans, neurons are the starting block for generalized intelligence. So the question was, can we interact with neurons in a way to harness that inherent intelligence?\u201d said Kagan.<\/p>\n\n\n\n Enter DishBrain<\/em>. Despite its name, the plated neurons and other brain cells are from an actual brain with consciousness. As for \u201cintelligence,\u201d the authors define it as the ability to gather information, collate the data, and adjust firing activity\u2014that is, how neurons process the data\u2014in a way that helps adapt towards a goal; for example, rapidly learning to place your hand on the handle of a piping hot pan without searing it on the rim.<\/p>\n\n\n\n The setup starts, true to its name, with a dish. The bottom of each one is covered with a computer chip, HD-MEA, that can record from stimulated electrical signals. Cells, either isolated from the cortex of mouse embryos or derived from human cells, are then laid on top. The dish is bathed in a nutritious fluid for the neurons to grow and thrive. As they mature, they grow from jiggly blobs into spindly shapes with vast networks of sinuous, interweaving branches.<\/p>\n\n\n\n Within two weeks, the neurons from mice self-organized into networks inside their tiny homes, bursting with spontaneous activity. Neurons from human origins\u2014skin cells or other brain cells\u2014took a bit longer, establishing networks in roughly a month or two.<\/p>\n\n\n\n Then came the training. Each chip was controlled by commercially available software, linking it to a computer interface. Using the system to stimulate neurons is similar to providing sensory data\u2014like those coming from your eyes as you focus on a moving ball. Recording the neurons\u2019 activity is the outcome\u2014that is, how they would react to (if inside a body) you moving your hand to hit the ball. DishBrain<\/em> was designed so that the two parts integrated in real time: similar to humans playing Pong, the neurons could in theory learn from past misses and adapt their behavior to hit the virtual \u201cball.\u201d<\/p>\n\n\n\n Here\u2019s how Pong goes. A ball bounces rapidly across the screen, and the player can slide a tiny vertical paddle\u2014which looks like a bold line\u2014up and down. Here, the \u201cball\u201d is represented by electrical zaps based on its location on the screen. This essentially translates visual information into electrical data for the biological neural network to process.<\/p>\n\n\n\n The authors then defined distinct regions of the chip for \u201csensation\u201d and \u201cmovements.\u201d One region, for example, captures incoming data from the virtual ball movement. A part of the \u201cmotor region\u201d then controls the virtual paddle to move up, whereas another causes it to move down. These assignments were arbitrary, the authors explained, meaning that the neurons within needed to adjust their firings to excel at a match.<\/p>\n\n\n\n So how do they learn? If the neurons \u201chit\u201d the ball\u2014that is, showing the corresponding type of electrical activity\u2014the team then zapped them at that location with the same frequency each time. It\u2019s a bit like establishing a \u201chabit\u201d for the neurons. If they missed the ball, then they were zapped with electrical noise that disrupted the neural network.<\/p>\n\n\n\n The strategy is based on a learning theory called the free energy principle, explained Kagan. Basically, it supposes that neurons hold \u201cbeliefs\u201d about their surroundings, and adjust and repeat their electrical activity so they can better predict the environment, either changing their \u201cbeliefs\u201d or their behavior.<\/p>\n\n\n\n The theory panned out. In just five minutes, both human and mice neurons rapidly improved their gameplay, including better rallies, fewer aces\u2014where the paddle failed to intercept the ball without a single hit\u2014and long gameplays with more than three consecutive hits. Surprisingly, mice neurons learned faster, though eventually they were outperformed by human ones.<\/p>\n\n\n\n The stimulations were critical for their learning. Separate experiments with DishBrain<\/em>without any electrical feedback performed far worse.<\/p>\n\n\n\n The study is a proof of concept that neurons in a dish can be a sophisticated learning machine, and even exhibit signs of sentience and intelligence, said Kagan. That\u2019s not to say they\u2019re conscious\u2014rather, they have the ability to adapt to a goal when \u201cembodied\u201d into a virtual environment.<\/p>\n\n\n\n Cortical Labs isn\u2019t the first to test the boundaries of the data processing power of isolated neurons. Back in 2008, Dr. Steve Potter at the Georgia Institute of Technology and team found<\/a> that with even just a few dozen electrodes, they could stimulate rat neurons to exhibit signs of learning in a dish.<\/p>\n\n\n\n DishBrain<\/em> has a leg up with thousands of electrodes compacted in each setup, and the company hopes to tap into its biological power to aid drug development. The system, or its future derivations, could potentially act as a micro-brain surrogate for testing neurological drugs, or gaining insights into the neurocomputation powers of different species or brain regions.<\/p>\n\n\n\n But the long-term vision is a \u201cliving\u201d bio-silicon computer hybrid. \u201cIntegrating neurons into digital systems may enable performance infeasible with silicon alone,\u201d the authors wrote. Kagan imagines developing \u201cbiological processing units\u201d that weave together the best of both worlds for more efficient computation\u2014and in the process, shed a light on the inner workings of our own minds.<\/p>\n\n\n\n \u201cThis is the start of a new frontier in understanding intelligence,\u201d said Kagan. \u201cIt touches on the fundamental aspects of not only what it means to be human, but what it means to be alive and intelligent at all, to process information and be sentient in an ever-changing, dynamic world.\u201d<\/p>\n\n\n\n Image Credit:\u00a0Cortical Labs<\/a><\/em><\/p>\n\n\n\n Author:<\/strong><\/p>\n\n\n\n Shelly Fan https:\/\/neurofantastic.com\/<\/a><\/p>\n\n\n\n Shelly Xuelai Fan is a neuroscientist-turned-science writer. She completed her PhD in neuroscience at the University of British Columbia, where she developed novel treatments for neurodegeneration. While studying biological brains, she became fascinated with AI and all things biotech. Following graduation, she moved to UCSF to study blood-based factors that rejuvenate aged brains. She is the co-founder of Vantastic Media, a media venture that explores science stories through text and video, and runs the award-winning blog NeuroFantastic.com. Her first book, “Will AI Replace Us?” (Thames & Hudson) was published in 2019.<\/p>\n\n\n\n Original Article<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Scientists just taught hundreds of thousands of neurons in a dish to play Pong. Using a series of strategically timed and placed electrical zaps, the neurons not only learned the game in a virtual environment, but played better over time\u2014with longer rallies and fewer misses\u2014showing a level of adaptation previously thought impossible. Why? Picture literally […]\n","protected":false},"author":1,"featured_media":3903,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"episode_type":"audio","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","footnotes":""},"categories":[13],"tags":[26,230],"series":[],"class_list":["post-3902","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articulos-ingles","tag-artificial-intelligence","tag-neurons"],"episode_featured_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2022\/10\/dishbrain_cortical_labs_neurons.jpeg","episode_player_image":"https:\/\/singularityumexico.com\/wp-content\/uploads\/2023\/05\/11711533-1673157178559-89a95be153719-4-scaled.jpg","download_link":"","player_link":"","audio_player":false,"episode_data":{"playerMode":"dark","subscribeUrls":{"apple_podcasts":{"key":"apple_podcasts","url":"","label":"Apple Podcasts","class":"apple_podcasts","icon":"apple-podcasts.png"},"stitcher":{"key":"stitcher","url":"","label":"Stitcher","class":"stitcher","icon":"stitcher.png"},"google_podcasts":{"key":"google_podcasts","url":"","label":"Google Podcasts","class":"google_podcasts","icon":"google-podcasts.png"},"spotify":{"key":"spotify","url":"","label":"Spotify","class":"spotify","icon":"spotify.png"}},"rssFeedUrl":"https:\/\/singularityumexico.com\/en\/feed\/podcast\/the-feedback-loop-by-singularity","embedCode":"Meet DishBrain<\/em><\/h2>\n\n\n\n
Ready Player DishBrain<\/em><\/h2>\n\n\n\n
Game On<\/h2>\n\n\n\n
\n\n\n\n800,000 Neurons in a Dish Learned to Play Pong in Just Five Minutes<\/a><\/blockquote>