{"691930":{"#nid":"691930","#data":{"type":"news","title":"A Neuroscientist Went Looking for Prediction. She Found Time Instead.","body":[{"value":"\u003Cdiv\u003E\u003Cp\u003EFew neuroscientists would dispute that the brain relies on prediction. From houseflies evading swatters to humans catching baseballs, living things are constantly anticipating what comes next. Understanding how the brain generates those predictions could help explain one of neuroscience\u0027s most enduring questions: how the brain builds internal models of the world that allow us to learn, adapt, and anticipate what comes next.\u003C\/p\u003E\u003Cp\u003E\u201cWithout actively predicting the world, we cannot survive,\u201d says \u003Ca href=\u0022https:\/\/people.research.gatech.edu\/farzaneh-najafi\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EFarzaneh Najafi\u003C\/a\u003E, an assistant professor in the \u003Ca href=\u0022https:\/\/biosciences.gatech.edu\/\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003ESchool of Biological Sciences\u003C\/a\u003E and a faculty affiliate of Georgia Tech\u0027s \u003Ca href=\u0022https:\/\/neuro.gatech.edu\/\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EInstitute for Neuroscience, Neurotechnology, and Society\u003C\/a\u003E. \u201cThere is quite some sensory-motor delay in the processing. We can\u2019t just sit there, wait for the brain to process our environment, and then react.\u201d\u003C\/p\u003E\u003Cp\u003EPart of the puzzle may lie in signals known as neural ramps. Almost like a drumroll leading up to a big reveal, neurons in some areas of the brain have shown gradual increases in activity immediately before a stimulus appears. For decades, researchers have interpreted this ramping activity as a neural signature of prediction, reflecting anticipation of an upcoming event.\u003C\/p\u003E\u003Cp\u003ERecently published in \u003Ca href=\u0022http:\/\/www.science.org\/doi\/10.1126\/sciadv.aed6417\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003E\u003Cem\u003EScience Advances\u003C\/em\u003E\u003C\/a\u003E, a new study from Najafi\u2019s lab reveals these signals may not be \u201cpredictions\u201d at all, but instead a way neurons track elapsed time.\u003C\/p\u003E\u003Cp\u003E\u201cSurprisingly,\u201d says Najafi, \u201cthe very first study from my lab called the Predictive Processing Lab showed that no, these are not prediction signals.\u201d\u003C\/p\u003E\u003Cp\u003EThe finding challenges a long-standing interpretation of one of neuroscience\u0027s most studied signals and reveals that the search for predictive neurons may lead to different circuits \u2014 or require different experiments to uncover.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Ch3\u003E\u003Cstrong\u003EThe Problem with Prediction\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThere is a challenge in separating simple time tracking from active prediction. Just because numbers on a stopwatch are increasing doesn\u2019t mean it\u2019s counting up to a specific event.\u003C\/p\u003E\u003Cp\u003ETo tease the problem apart, the team designed a series of experiments that progressively stripped prediction out of the equation.\u003C\/p\u003E\u003Cp\u003EWorking with mice, the researchers presented audio and visual cues at carefully controlled intervals. Some appeared at regular, hence predictable, times, while others arrived unpredictably. If neurons are making predictions, their activity should look different when events are predictable versus when they are not.\u003C\/p\u003E\u003Cp\u003EBut that\u2019s not what they found. Even when the researchers introduced errors into those predictable patterns, activity remained largely the same.\u003C\/p\u003E\u003Cp\u003E\u201cIt was in the first year of collecting data in my newly established lab that my student, Yicong Huang, started showing me the data and I was shocked: how come we are not seeing a difference between the expected case and the unexpected case?\u201d Najafi recalls. \u201cBecause the entire theory is that there is a difference.\u201d\u003C\/p\u003E\u003Cp\u003EThe team found even more definitive evidence by monitoring \u201cnaive\u201d mice that had never seen the stimuli before. The brain must learn a pattern before it can anticipate it, yet they found that these neural ramps were present even in the first few trials.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Ch3\u003E\u003Cstrong\u003EDrumroll, Please\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIf these signals aren\u2019t predictions, then what\u2019s happening?\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cWhat we are seeing are pure sensory signals,\u201d she says. \u201cThey are not about predicting the timing of the upcoming stimulus. They\u0027re about encoding the time that is elapsed.\u201d\u003C\/p\u003E\u003Cp\u003ENajafi thinks that what neuroscientists have long interpreted as an anticipatory \u201cbuildup\u201d to a future event is actually a \u201crelaxation\u201d from the past. Rather than a drummer rolling up to a specific event, imagine one who is always rolling. Each stimulus briefly interrupts the performance before the rhythm gradually returns.\u003C\/p\u003E\u003Cp\u003EBut they found that not every neuron behaves like a drummer. While \u201cdrummers\u201d recover their interrupted rhythm after a stimulus, other neurons operate more like a reverberating gong, firing strongly after a stimulus before gradually quieting down.\u003C\/p\u003E\u003Cp\u003E\u201cThe beautiful part of this story is that neurons don\u0027t all do the same thing,\u201d Najafi says. \u201cOne neuron ramps up quickly, another more slowly, another with a completely different time course. When you put that heterogeneous population together, you get a very robust readout of time.\u201d\u003C\/p\u003E\u003Cp\u003EThe finding may also have implications for how neuroscientists think the brain represents time itself.\u003C\/p\u003E\u003Cp\u003E\u201cOur findings support the theory that time representation in the brain is an intrinsic property of neurons,\u201d says Najafi. Because the signals appeared even in na\u00efve mice and in sensory brain regions, the results suggest that timing may emerge from the properties of neurons themselves, rather than from a specialized timing system elsewhere in the brain.\u003C\/p\u003E\u003Cp\u003EFor Najafi, the study doesn\u0027t close the case on predictive processing. Time, after all, is an important variable to track if you want to make predictions. Instead, it opens more questions about when and where those signals emerge.\u003C\/p\u003E\u003Cp\u003E\u201cMaybe we didn\u0027t find them because this was a passive perception task, meaning mice just passively received stimuli without being instructed to attend to them. Maybe we need active perception or an active movement task, and that\u0027s when we can extract these predictive signals from the brain. Alternatively, we may need to search other brain areas to find neural signatures of temporal predictions.\u201d\u003C\/p\u003E\u003Cp\u003E\u201cDo I believe now that the brain is not doing predictive processing? Absolutely not,\u201d Najafi says. \u201cBut before we say we\u0027ve found evidence for a theory, we really need to do multiple carefully designed experiments. We need to attack this from many different angles.\u201d\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003E\u003Cem\u003EFunding: This research was supported by the Whitehall Foundation, the Research Corporation for Science Advancement, the Chan Zuckerberg Initiative, and the Georgia Institute of Technology.\u003C\/em\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EDOI: 10.1126\/sciadv.aed6417\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EA Georgia Tech study challenges a decades-old explanation for one of neuroscience\u0027s most famous signals, revealing an intrinsic neural code for tracking time.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A Georgia Tech study challenges a decades-old explanation for one of neuroscience\u0027s most famous signals, revealing an intrinsic neural code for tracking time."}],"uid":"35575","created_gmt":"2026-08-24 19:39:14","changed_gmt":"2026-08-24 19:50:13","author":"adavidson38","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-08-24T00:00:00-04:00","iso_date":"2026-08-24T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"680972":{"id":"680972","type":"image","title":"Najafi-SciAdv.jpeg","body":"\u003Cdiv\u003EGeorgia Tech neuroscientist Farzaneh Najafi studies how the brain predicts future events. A new study from her lab challenges a long-standing interpretation of one of neuroscience\u0027s most famous \u0022prediction signals,\u0022 suggesting it may instead help the brain track elapsed time. Photo via Georgia Tech College of Sciences.\u003C\/div\u003E","created":"1787600538","gmt_created":"2026-08-24 19:42:18","changed":"1787600957","gmt_changed":"2026-08-24 19:49:17","alt":"Farzaneh Najafi standing outside smiling with her arms crossed. Green foliage is visible in the background.","file":{"fid":"265314","name":"Najafi-SciAdv.jpeg","image_path":"\/sites\/default\/files\/2026\/08\/24\/Najafi-SciAdv.jpeg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/08\/24\/Najafi-SciAdv.jpeg","mime":"image\/jpeg","size":7788922,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/08\/24\/Najafi-SciAdv.jpeg?itok=DPVq2zuE"}}},"media_ids":["680972"],"related_links":[{"url":"https:\/\/neuro.gatech.edu\/molecules-mind-farzaneh-najafi-receives-multiple-awards-cognitive-research","title":"From Molecules to Mind: Farzaneh Najafi Receives Multiple Awards for Cognitive Research"},{"url":"https:\/\/neuro.gatech.edu\/nathan-mcdonald-and-farzaneh-najafi-awarded-curci-foundation-grants","title":"Nathan McDonald and Farzaneh Najafi Awarded Curci Foundation Grants"},{"url":"https:\/\/neuro.gatech.edu\/georgia-tech-researchers-make-waves-worlds-largest-neuroscience-conference","title":"Georgia Tech Researchers Make Waves at the World\u2019s Largest Neuroscience Conference"}],"groups":[{"id":"1278","name":"College of Sciences"},{"id":"66220","name":"Neuro"},{"id":"1292","name":"Parker H. Petit Institute for Bioengineering and Bioscience (IBB)"},{"id":"1188","name":"Research Horizons"},{"id":"1275","name":"School of Biological Sciences"}],"categories":[{"id":"138","name":"Biotechnology, Health, Bioengineering, Genetics"},{"id":"146","name":"Life Sciences and Biology"},{"id":"135","name":"Research"}],"keywords":[{"id":"187915","name":"go-researchnews"},{"id":"172970","name":"go-neuro"},{"id":"192253","name":"cos-neuro"}],"core_research_areas":[{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"193656","name":"Neuro Next Initiative"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EWriter and Media Contact:\u003Cbr\u003E\u003Ca href=\u0022mailto:audra.davidson@research.gatech.edu\u0022\u003EAudra Davidson\u003C\/a\u003E\u003Cbr\u003ECommunications Manager\u003Cbr\u003EInstitute for Neuroscience, Neurotechnology, and Society (INNS)\u003C\/p\u003E","format":"limited_html"}],"email":["audra.davidson@research.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}