{"id":90530,"date":"2025-05-10T16:56:08","date_gmt":"2025-05-10T16:56:08","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/90530\/"},"modified":"2025-05-10T16:56:08","modified_gmt":"2025-05-10T16:56:08","slug":"ai-learns-to-decode-neuron-types-from-brain-signals-with-95-accuracy","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/90530\/","title":{"rendered":"AI Learns to Decode Neuron Types From Brain Signals With 95% Accuracy"},"content":{"rendered":"<p><strong>Summary: <\/strong>Scientists have developed an AI algorithm that can identify different types of neurons from brain activity recordings with 95% accuracy, without needing genetic tools. By tagging neurons with light-sensitive markers and recording their unique electrical signatures, researchers created a training library that allowed the AI to distinguish neuron types in both mice and monkeys.<\/p>\n<p>This breakthrough addresses a century-old challenge in neuroscience and opens the door to better understanding how different neurons contribute to behavior and disease. The tool could one day improve neural implants, help decode disorders like epilepsy, and refine how we study the brain in both animals and humans.<\/p>\n<p><strong>Key Facts:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>AI Breakthrough:<\/strong> A new algorithm distinguishes neuron types from electrical activity with high precision.<\/li>\n<li><strong>Cross-Species Utility:<\/strong> Validated in both mice and monkeys, with potential for human application.<\/li>\n<li><strong>Open Access:<\/strong> The database and AI tool are freely available to the global research community.<\/li>\n<\/ul>\n<p><strong>Source: <\/strong>UCL<\/p>\n<p><strong>Brains are made up of many different types of neurons (nerve cells in the brain), each of which are thought to play different roles in processing information. Scientists have long been able to use electrodes to record the activity of neurons by detecting the electrical \u2018spikes\u2019 that they generate while performing brain functions.<\/strong><\/p>\n<p>Although recording spikes has proved invaluable for monitoring the activity of individual neurons deep in the brain, until now the method has been \u2018blind\u2019 to the type of neuron being recorded \u2013 making it impossible to identify how different neurons contribute to the brain\u2019s overall operation.<\/p>\n<p>In a new study,\u00a0published in\u00a0Cell, the research team have overcome this problem by identifying the distinct \u2018electrical signatures\u2019 of different neuron types in the mouse brain, using brief pulses of blue light to trigger spikes in specific cell types (a method called optogenetics).<\/p>\n<p>They created a library of the different electrical signatures for each type of neuron, which then allowed them to train an AI algorithm that can automatically recognise five different types of neurons with 95% accuracy without further need for genetic tools.<\/p>\n<p>The algorithm was also validated on brain recording data from monkeys.<\/p>\n<p>The researchers say they have overcome a major hurdle in being able to use the technology to study neurological conditions such as epilepsy, but that there is still \u201ca long way\u201d to go before it can be used in practical applications.<\/p>\n<p>Dr Maxime Beau, co-first author of the study from the UCL Wolfson Institute for Biomedical Research, said: \u201cFor decades, neuroscientists have struggled with the fundamental problem of reliably identifying the many different types of neurons that are simultaneously active during behaviour.<\/p>\n<p>\u201cOur approach now enables us to identify neuron types with over 95% accuracy in mice and in monkeys.<\/p>\n<p>\u201cThis advance will enable researchers to record brain circuits as they perform complex behaviours such as movement. Like logic gates on a computer chip, neurons in the brain are elementary computing units that come in several types.<\/p>\n<p>\u201cOur method provides a tool to identify many of the brain\u2019s logic gates in action at the same time. Before, it could only be done one at a time, and at much greater cost.\u201d<\/p>\n<p>The authors say the fact that the algorithm can be applied across different species gives it huge potential for being expanded to other animals and, eventually, to humans.<\/p>\n<p>In the short term, the new technique means that, instead of requiring complex genetic engineering to study the brain, researchers could use any normal animal to study what different neurons do and how they interact with one another to generate behaviour.<\/p>\n<p>One of the ultimate aims is to be able to study neurological and neuropsychiatric disorders such as epilepsy, autism and dementia, many of which are thought to involve changes to the way different cell types in the brain interact.<\/p>\n<p>Professor Beverley Clark a senior author of the study from UCL Wolfson Institute for Biomedical Research, said: \u201cJust as many different instruments in an orchestra contribute to the sound of a symphony, the brain relies on many distinct neuron types to create the complex behaviour that humans and other animals exhibit.<\/p>\n<p>\u201cOur work is analogous to learning the sound that each instrument makes and then teaching an algorithm to recognise the contribution of each of them to a symphony.<\/p>\n<p>\u201cBeing able to observe this \u2018neural symphony\u2019 of the brain in action has been a fundamental challenge in neuroscience for over 100 years, and we now have a method for reliably doing this.<\/p>\n<p>\u201cAlthough the technology is a long way from being able to be used to study neurological conditions such as epilepsy, we\u2019ve now overcome a major hurdle to reaching that goal.<\/p>\n<p>In fact, some recordings of living human brain activity have already been recorded in patients during surgery, and our technique could be used to study those recordings to better understand how our brains work, first in health and then in disease.\u201d<\/p>\n<p>Improved understanding of how our brains work could pave the way for some ground-breaking advances in medical science, some of which are already on the horizon.<\/p>\n<p>Human brain-to-computer interfaces, or neural implants, are one such possibility. Ongoing research at the UCSF Weill Institute for Neurosciences, for example, has enabled a paralysed man to control a robotic arm using a neural implant for a record seven months.<\/p>\n<p>Like the current study, this work was also informed by studying the electrical patterns in the brains of animals and using AI to automatically recognise these patterns.<\/p>\n<p>The authors say the new technique to differentiate neuron types could help to improve neural implants by more accurately recording which types of cells are involved in particular actions, so that the implant can more easily recognise specific signals and generate the appropriate response.<\/p>\n<p>Key to this technology is understanding how our brains work when they\u2019re healthy, so that any damage can be compensated for. If a person had a stroke and part of their brain was damaged, for example, you would need to understand how that bit worked before you could consider designing an implant to replicate that functionality.<\/p>\n<p>Professor Michael H\u00e4usser, a senior author of the study from UCL Division of Medicine and The University of Hong Kong, said: \u201cThis project came to life thanks to the convergence of three critical innovations: using molecular biology to successfully \u2018tag\u2019 different neuron types using light, developments in silicon probe recording technology, and of course the fast-paced improvements in deep learning.<\/p>\n<p>\u201cCrucially, the synergy in our team was absolutely instrumental. The partner labs at UCL, Baylor, Duke and Bar Ilan University have all contributed critical pieces to the puzzle. Just like the brain, the whole is larger than the sum of its parts.\u201d<\/p>\n<p>The database gathered by the team is freely available and the algorithm is open source, meaning scientists from across the world can use these resources for neurological research.<\/p>\n<p><strong>Funding: <\/strong>This research was funded by funding from Wellcome, National Institutes of Health (NIH), European Research Council (ERC), and the European Union\u2019s Horizon 2020 research and innovation programme.<\/p>\n<p>About this AI and neuroscience research news<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Author: <\/strong><a href=\"https:\/\/www.ucl.ac.uk\/\" target=\"_blank\" rel=\"noreferrer noopener\">Matt Midgley<\/a><br \/><strong>Source: <\/strong><a href=\"https:\/\/www.ucl.ac.uk\/\" target=\"_blank\" rel=\"noreferrer noopener\">UCL<\/a><br \/><strong>Contact: <\/strong>Matt Midgley \u2013 UCL<br \/><strong>Image: <\/strong>The image is credited to Neuroscience News<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Original Research: <\/strong>Open access.<br \/>\u201c<a href=\"https:\/\/doi.org\/10.1016\/j.cell.2025.01.041\" target=\"_blank\" rel=\"noreferrer noopener\">A deep learning strategy to identify cell types across species from high-density extracellular recordings<\/a>\u201d by Beverley Clark et al. Cell<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p><strong>A deep learning strategy to identify cell types across species from high-density extracellular recordings<\/strong><\/p>\n<p>High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type.<\/p>\n<p>Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties.<\/p>\n<p>We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers.<\/p>\n<p>We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron.<\/p>\n<p>The classifier\u2019s predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species.<\/p>\n<p>Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.<\/p>\n","protected":false},"excerpt":{"rendered":"Summary: Scientists have developed an AI algorithm that can identify different types of neurons from brain activity recordings&hellip;\n","protected":false},"author":2,"featured_media":90531,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3163],"tags":[323,1942,215,3725,3690,219,220,53,2773,16,15],"class_list":{"0":"post-90530","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-brain-research","11":"tag-deep-learning","12":"tag-machine-learning","13":"tag-neurobiology","14":"tag-neuroscience","15":"tag-technology","16":"tag-ucl","17":"tag-uk","18":"tag-united-kingdom"},"share_on_mastodon":{"url":"","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/90530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=90530"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/90530\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/90531"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=90530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=90530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=90530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}