638 citations · 3k across the 32 of their papers we have counts for
35 papers
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Charles Y. Zhang, Yuanjia Yang, Aidan Sirbu +25
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex behavior, kinematic trajectories alone provi…
PySensors 2.0: A Python Package for Sparse Sensor Placement
Niharika Karnik, Yash Bhangale, Mohammad G. Abdo +6
PySensors is a Python package for selecting and placing a sparse set of sensors for reconstruction and classification tasks. In this major update to PySensors, we introduce spatial…
ODIN: Open Data In Neurophysiology: Advancements, Solutions & Challenges
Colleen J. Gillon, Cody Baker, Ryan Ly +5
Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and…
Decoding complexity: how machine learning is redefining scientific discovery
Ricardo Vinuesa, Paola Cinnella, Jean Rabault +10
As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an ess…
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton +2
The discovery of governing differential equations from data is an open frontier in machine learning. The sparse identification of nonlinear dynamics (SINDy) \citep{brunton_discover…
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Jennifer J. Sun, Lili Karashchuk, Amil Dravid +9
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised…
Network inference via process motifs for lagged correlation in linear stochastic processes
Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton
A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in a…
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Sabera Talukder, Jennifer J. Sun, Matthew Leonard +2
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings…
Neural-inspired Measurement Observability
Burak Boyacıoğlu, Alice C. Schwarze, Bingni W. Brunton +1
The neural encoding by biological sensors of flying insects, which prefilters stimulus data before sending it to the central nervous system in the form of voltage spikes, enables s…
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Urban Fasel, J. Nathan Kutz, Bingni W. Brunton +1
Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. I…
Data-driven unsteady aeroelastic modeling for control
Michelle Hickner, Urban Fasel, Aditya G. Nair +2
Aeroelastic structures, from insect wings to wind turbine blades, experience transient unsteady aerodynamic loads that are coupled to their motion. Effective real-time control of f…
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes
Satpreet Harcharan Singh, Floris van Breugel, Rajesh P. N. Rao +1
Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind d…
PySensors: A Python Package for Sparse Sensor Placement
Brian M. de Silva, Krithika Manohar, Emily Clark +3
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-…
Structured Time-Delay Models for Dynamical Systems with Connections to Frenet-Serret Frame
Seth M. Hirsh, Sara M. Ichinaga, Steven L. Brunton +2
Time-delay embeddings and dimensionality reduction are powerful techniques for discovering effective coordinate systems to represent the dynamics of physical systems. Recently, it…
Numerical differentiation of noisy data: A unifying multi-objective optimization framework
Floris van Breugel, J. Nathan Kutz, Bingni W. Brunton
Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or…
Go with the FLOW: Visualizing spatiotemporal dynamics in optical widefield calcium imaging
Nathaniel J Linden, Dennis R Tabuena, Nicholas A Steinmetz +3
Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunit…
Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation
George Stepaniants, Bingni W. Brunton, J. Nathan Kutz
Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given dat…
Phase-based control of periodic fluid flows
Aditya G. Nair, Kunihiko Taira, Bingni W. Brunton +1
Fluid flows play a central role in scientific and technological development, and many of these flows are characterized by a dominant oscillation, such as the vortex shedding in the…
Phase-consistent dynamic mode decomposition from multiple overlapping spatial domains
Aditya G. Nair, Benjamin Strom, Bingni W. Brunton +1
Dynamic mode decomposition (DMD) provides a principled approach to extract physically interpretable spatial modes from time-resolved flow field data, along with a linear model for…
Learning dominant physical processes with data-driven balance models
Jared L. Callaham, James V. Koch, Bingni W. Brunton +2
Throughout the history of science, physics-based modeling has relied on judiciously approximating observed dynamics as a balance between a few dominant processes. However, this tra…
Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
Satpreet H. Singh, Steven M. Peterson, Rajesh P. N. Rao +1
Recent technological advances in brain recording and artificial intelligence are propelling a new paradigm in neuroscience beyond the traditional controlled experiment. Rather than…
Centering Data Improves the Dynamic Mode Decomposition
Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz +1
Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by line…
Time-varying Autoregression with Low Rank Tensors
Kameron Decker Harris, Aleksandr Aravkin, Rajesh Rao +1
We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotempora…