1 code implementation • 3 Sep 2023 • T. Anderson Keller, Lyle Muller, Terrence Sejnowski, Max Welling
Traveling waves of neural activity have been observed throughout the brain at a diversity of regions and scales; however, their precise computational role is still debated.
no code implementations • 30 May 2023 • Devdhar Patel, Terrence Sejnowski, Hava Siegelmann
We present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure.
no code implementations • 25 Dec 2022 • Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence Sejnowski, Hava Siegelmann
Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands.
no code implementations • 15 Oct 2022 • Anthony Zador, Sean Escola, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Koerding, Alexei Koulakov, Yann Lecun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
no code implementations • 28 Jul 2022 • Terrence Sejnowski
If so, then by studying interviews we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs.
no code implementations • 11 Oct 2021 • Anish A. Sarma, Jing Shuang Li, Josefin Stenberg, Gwyneth Card, Elizabeth S. Heckscher, Narayanan Kasthuri, Terrence Sejnowski, John C. Doyle
In biology, in addition to typical feedback between plant and controller, we observe feedback pathways within control systems, which we call internal feedback pathways (IFPs), that are often very complex.
1 code implementation • 10 Sep 2021 • Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions.
1 code implementation • 28 May 2019 • Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
Moment closure methods are used to approximate a subset of low order moments by terminating the hierarchy at some order and replacing higher order terms with functions of lower order ones.
1 code implementation • 2 Mar 2018 • Oliver K. Ernst, Thomas Bartol, Terrence Sejnowski, Eric Mjolsness
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant.
Biological Physics Statistical Mechanics