8 citations · 8 across the 4 of their papers we have counts for
7 papers
BrainViewer: interacting with spatial connectome data at the mesoscale
Seth Daetwiler, Angus Read, Jessica Stillwell +1
Scientists construct connectomes, comprehensive descriptions of neuronal connections across a brain, in order to better understand and model brain function. Interactive visualizati…
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…
Greedy low-rank algorithm for spatial connectome regression
Patrick Kürschner, Sergey Dolgov, Kameron Decker Harris +1
Recovering brain connectivity from tract tracing data is an important computational problem in the neurosciences. Mesoscopic connectome reconstruction was previously formulated as…
Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics
P. S. Dodds, E. M. Clark, S. Desu +11
We demonstrate that the concerns expressed by Garcia et al. are misplaced, due to (1) a misreading of our findings in [1]; (2) a widespread failure to examine and present words in…
Limited Imitation Contagion on Random Networks: Chaos, Universality, and Unpredictability
Peter Sheridan Dodds, Kameron Decker Harris, Christopher M. Danforth
We study a family of binary state, socially-inspired contagion models which incorporate imitation limited by an aversion to complete conformity. We uncover rich behavior in our mod…