no code implementations • 28 Apr 2024 • Noga Mudrik, Eva Yezerets, Yenho Chen, Christopher Rozell, Adam Charles
Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging.
no code implementations • 13 Feb 2024 • Gal Mishne, Adam Charles
Optical imaging of the brain has expanded dramatically in the past two decades.
no code implementations • 19 Jan 2022 • Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan, Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
1 code implementation • 8 Nov 2017 • Michael Shvartsman, Narayanan Sundaram, Mikio C. Aoi, Adam Charles, Theodore C. Wilke, Jonathan D. Cohen
We show how the matrix-variate normal (MN) formalism can unify some of these methods into a single framework.
no code implementations • 26 May 2016 • Adam Charles, Dong Yin, Christopher Rozell
In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs.
no code implementations • 22 Jul 2015 • Adam Charles, Aurele Balavoine, Christopher Rozell
Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.