Uploaded December 2023 | Updated September 2026, 2 weeks ago
Video abstract for “Arousal as a universal embedding for spatiotemporal brain dynamics”
by Ryan V. Raut, Zachary P. Rosenthal, Xiaodan Wang, Hanyang Miao, Zhanqi Zhang, Jin-Moo Lee, Marcus E. Raichle, Adam Q. Bauer, Steven L. Brunton, Bingni W. Brunton, and J. Nathan Kutz
biorxiv.org/content/10.1101/2023.11.06.565918v2
Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology—widely interpreted to reflect regionally-specific neural communication—are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.
Video abstract for “Arousal as a universal embedding for spatiotemporal brain dynamics”
by Ryan V. Raut, Zachary P. Rosenthal, Xiaodan Wang, Hanyang Miao, Zhanqi Zhang, Jin-Moo Lee, Marcus E. Raichle, Adam Q. Bauer, Steven L. Brunton, Bingni W. Brunton, and J. Nathan Kutz
biorxiv.org/content/10.1101/2023.11.06.565918v2
Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology—widely interpreted to reflect regionally-specific neural communication—are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.



![Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model
This video gives an intro example of how we model complex systems that change in time, using matrices and vectors. Specifically, I build a toy model for the weather, where the probability of the weather today being (R)ainy, (N)ice, or (C)loudy is stored in a vector [R, N, C]. This probability of the weather being in one of these states tomorrow is then updated by multiplying this vector by a probability matrix.
Code examples are given in Python and Matlab.
Playlist: https://www.youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
Course Website: http://faculty.washington.edu/sbrunton/me564/
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Overview
1:07 Building a simple weather model
5:00 Modeling the state as a vector
6:50 Writing the dynamical system update rule as a matrix
14:07 Matlab code example
23:43 Python code example
38:24 Teaser of how to make system more realistic Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model](https://i.ytimg.com/vi/K-8F_zDMDUI/mqdefault.jpg)




![New Advances in Artificial Intelligence and Machine Learning
[Tier 1, Lecture 3] This video describes modern advances in machine learning and artificial intelligence, which are rapidly evolving technologies. Topics include generative AI (diffusion models, DALL-E 2, ChatGPT, etc.), reinforcement learning, computer vision, etc.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
0:00 Overview
1:04 Image Classification
3:14 The Importance of Training Data
7:19 Generative Images
8:22 Image Captioning
9:18 DALL-E 2
11:08 History of Deep Dream
14:15 Text Generation and NLP
16:24 ChatGPT and LLMs
18:55 What is ML good at?
20:36 Reinforcement Learning and Atari
24:45 Chaos and Weather
26:45 Outro New Advances in Artificial Intelligence and Machine Learning](https://i.ytimg.com/vi/NQkSH_CBPq8/mqdefault.jpg)

