{"585936":{"#nid":"585936","#data":{"type":"event","title":"Discovering Dynamic Computations in the Brain from Large-scale Neural Recordings","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETatiana Engel, Stanford University\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nNeural responses and behavior are influenced by internal brain states, such as arousal, vigilance, or task context. Ongoing variations of these\u0026nbsp;internal states affect global patterns of neural activity, giving rise to\u0026nbsp;apparent\u0026nbsp;variability of neural responses to sensory stimuli, from trial-to-trial\u0026nbsp;and across time within single trials. Demultiplexing these endogenously generated and\u0026nbsp;externally driven signals\u0026nbsp;proved difficult with traditional techniques based on trial-averaged responses of single neurons,\u0026nbsp;which\u0026nbsp;dismiss neural variability\u0026nbsp;as noise.\u0026nbsp;In this talk, I will describe my recent work leveraging multi-electrode neural activity recordings and\u0026nbsp;computational models\u0026nbsp;to uncover how internal brain\u0026nbsp;states interact\u0026nbsp;with goal-directed behavior.\u0026nbsp;I will show that ensemble neural activity within single columns of\u0026nbsp;the primate visual cortex spontaneously\u0026nbsp;fluctuates between phases of vigorous (On) and faint (Off) spiking. These\u0026nbsp;endogenous On-Off dynamics, reflecting global changes in arousal, are\u0026nbsp;also\u0026nbsp;modulated at a local scale during spatial\u0026nbsp;attention and predict behavioral performance. I will also demonstrate\u0026nbsp;that these On-Off dynamics provide a single unifying mechanism that explains\u0026nbsp;general\u0026nbsp;features of\u0026nbsp;correlated\u0026nbsp;variability\u0026nbsp;classically observed in cortical responses (e.g.,\u0026nbsp;changes in neural\u0026nbsp;correlations during attention).\u0026nbsp;I will conclude by\u0026nbsp;sketching out a roadmap for developing a general\u0026nbsp;theory that will allow us\u0026nbsp;to discover dynamic computations from large-scale neural recordings and to\u0026nbsp;link these computations\u0026nbsp;to behavior.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nNeural responses and behavior are influenced by internal brain states, such as arousal, vigilance, or task context. Ongoing variations of these\u0026nbsp;internal states affect global patterns of neural activity, giving rise to\u0026nbsp;apparent\u0026nbsp;variability of neural responses to sensory stimuli, from trial-to-trial\u0026nbsp;and across time within single trials. Demultiplexing these endogenously generated and\u0026nbsp;externally driven signals\u0026nbsp;proved difficult with traditional techniques based on trial-averaged responses of single neurons,\u0026nbsp;which\u0026nbsp;dismiss neural variability\u0026nbsp;as noise.\u0026nbsp;In this talk, I will describe my recent work leveraging multi-electrode neural activity recordings and\u0026nbsp;computational models\u0026nbsp;to uncover how internal brain\u0026nbsp;states interact\u0026nbsp;with goal-directed behavior.\u0026nbsp;I will show that ensemble neural activity within single columns of\u0026nbsp;the primate visual cortex spontaneously\u0026nbsp;fluctuates between phases of vigorous (On) and faint (Off) spiking. These\u0026nbsp;endogenous On-Off dynamics, reflecting global changes in arousal, are\u0026nbsp;also\u0026nbsp;modulated at a local scale during spatial\u0026nbsp;attention and predict behavioral performance. I will also demonstrate\u0026nbsp;that these On-Off dynamics provide a single unifying mechanism that explains\u0026nbsp;general\u0026nbsp;features of\u0026nbsp;correlated\u0026nbsp;variability\u0026nbsp;classically observed in cortical responses (e.g.,\u0026nbsp;changes in neural\u0026nbsp;correlations during attention).\u0026nbsp;I will conclude by\u0026nbsp;sketching out a roadmap for developing a general\u0026nbsp;theory that will allow us\u0026nbsp;to discover dynamic computations from large-scale neural recordings and to\u0026nbsp;link these computations\u0026nbsp;to behavior.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Tatiana Engel, Stanford University"}],"uid":"27964","created_gmt":"2017-01-12 18:32:20","changed_gmt":"2017-04-13 21:13:19","author":"Jasmine Martin","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2017-01-31T10:00:00-05:00","event_time_end":"2017-01-31T11:00:00-05:00","event_time_end_last":"2017-01-31T11:00:00-05:00","gmt_time_start":"2017-01-31 15:00:00","gmt_time_end":"2017-01-31 16:00:00","gmt_time_end_last":"2017-01-31 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1275","name":"School of Biological Sciences"}],"categories":[],"keywords":[{"id":"4896","name":"College of Sciences"},{"id":"166892","name":"School of Biological Sciences Seminar"},{"id":"76431","name":"Georgia Tech Neuro"},{"id":"25121","name":"gt neuro"},{"id":"17641","name":"gtneuro"},{"id":"166937","name":"School of Physics"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EIf you have questions about logistics or would like to set up an appointment with the speaker, please contact the School of Biological Sciences\u0026#39; administrative office at \u003Ca href=\u0022mailto:bio-admin@biology.gatech.edu\u0022\u003Ebio-admin@lists.gatech.edu\u003C\/a\u003E.\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}