Uploaded December 2025 | Updated September 2026, 3 weeks ago
2025 Research Showcase
Title: Causality, Learning, and Communication in the Brain
Speaker: Matt Golub (Paul G. Allen School of Computer Science & Engineering)
Date: October 29, 2025
Abstract: The Systems Neuroscience and AI Lab (SNAIL) develops and applies machine learning techniques to understand how our brains drive our capacities to sense, feel, think, and act. We design computational models and analytical tools to reverse-engineer neural computation, drawing on large-scale recordings of spiking activity from populations of hundreds or thousands of neurons in the brain. In this talk, I will highlight recent and ongoing SNAIL efforts that view large neural populations as dynamical systems whose structure and activity evolve lawfully over time. I will begin by describing how this perspective has enabled us to efficiently identify causal structure in biological neural networks, which is revealed through neural responses to external perturbations that are algorithmically chosen to be maximally informative. I will then discuss deep learning approaches to dynamical systems modeling that allow us to uncover how neural population activity changes during learning and how multiple brain regions communicate with each other to support distributed computation. Together, these advances are helping to elucidate fundamental principles by which the brain organizes its dynamics to support flexible and intelligent behavior.
Biography: Matt Golub joined the University of Washington in 2022, where he is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering. Matt directs the UW Systems Neuroscience & AI Lab (SNAIL, aka Golub Lab), which focuses on research at the intersection of neuroscience, neuroengineering, machine learning, and data science. Projects in the lab design computational models, algorithms, and experiments to investigate how single-trial neural population activity drives our abilities to generate movements, make decisions, and learn from experience. Outside of research, Matt teaches undergraduate and graduate courses on machine learning and its application to neuroscience and neuroengineering. Previously, Matt was a Postdoctoral Fellow at Stanford University, where he was advised by Krishna Shenoy, Bill Newsome, and David Sussillo. His postdoctoral work advanced deep learning and dynamical systems techniques for understanding how neural population activity supports decision-making, learning, and flexible computation. This work was recognized by a K99/R00 Pathway to Independence Award from the National Institutes of Health. Matt completed his PhD at Carnegie Mellon University, where he was advised by Byron Yu and Steve Chase. There, Matt established brain-computer interfaces as a scientific paradigm for investigating the neural bases of learning and feedback motor control. His PhD dissertation received the Best Thesis Award from the Department of Electrical & Computer Engineering.
This video is closed captioned.
2025 Research Showcase
Title: Causality, Learning, and Communication in the Brain
Speaker: Matt Golub (Paul G. Allen School of Computer Science & Engineering)
Date: October 29, 2025
Abstract: The Systems Neuroscience and AI Lab (SNAIL) develops and applies machine learning techniques to understand how our brains drive our capacities to sense, feel, think, and act. We design computational models and analytical tools to reverse-engineer neural computation, drawing on large-scale recordings of spiking activity from populations of hundreds or thousands of neurons in the brain. In this talk, I will highlight recent and ongoing SNAIL efforts that view large neural populations as dynamical systems whose structure and activity evolve lawfully over time. I will begin by describing how this perspective has enabled us to efficiently identify causal structure in biological neural networks, which is revealed through neural responses to external perturbations that are algorithmically chosen to be maximally informative. I will then discuss deep learning approaches to dynamical systems modeling that allow us to uncover how neural population activity changes during learning and how multiple brain regions communicate with each other to support distributed computation. Together, these advances are helping to elucidate fundamental principles by which the brain organizes its dynamics to support flexible and intelligent behavior.
Biography: Matt Golub joined the University of Washington in 2022, where he is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering. Matt directs the UW Systems Neuroscience & AI Lab (SNAIL, aka Golub Lab), which focuses on research at the intersection of neuroscience, neuroengineering, machine learning, and data science. Projects in the lab design computational models, algorithms, and experiments to investigate how single-trial neural population activity drives our abilities to generate movements, make decisions, and learn from experience. Outside of research, Matt teaches undergraduate and graduate courses on machine learning and its application to neuroscience and neuroengineering. Previously, Matt was a Postdoctoral Fellow at Stanford University, where he was advised by Krishna Shenoy, Bill Newsome, and David Sussillo. His postdoctoral work advanced deep learning and dynamical systems techniques for understanding how neural population activity supports decision-making, learning, and flexible computation. This work was recognized by a K99/R00 Pathway to Independence Award from the National Institutes of Health. Matt completed his PhD at Carnegie Mellon University, where he was advised by Byron Yu and Steve Chase. There, Matt established brain-computer interfaces as a scientific paradigm for investigating the neural bases of learning and feedback motor control. His PhD dissertation received the Best Thesis Award from the Department of Electrical & Computer Engineering.
This video is closed captioned.
![[Audio Descriptions] I Am CSE: Kyle Johnson](https://i.ytimg.com/vi/dYksN04tkCA/mqdefault.jpg "[Audio Descriptions] I Am CSE: Kyle Johnson
Allen School graduate student Kyle Johnson of the UW’s Mobile Intelligence Lab and Autonomous Insect Robotics Lab, shares his work on origami microfliers, tiny battery-free robots inspired by nature that can be used for environmental monitoring, as well as his role in A Vision for Engineering Leadership and Access (AVELA) focused on engaging underserved youth.
This video is closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/tyOcyn2LuIw.")
