Analysis of complex neural circuits with nonlinear multidimensional hidden state models
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Gr...
Main Authors: | Altshuler, Alex, Sholes, Jacquelyn E. C., Friedman, Alexander, Slocum, Joshua Foster, Tyulmankov, Danil, Gibb, Leif G., Ruangwises, Suthee, Shi, Qinru, Toro Arana, Sebastian, Beck, Dirk W., Graybiel, Ann M |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
Format: | Article |
Language: | en_US |
Published: |
National Academy of Sciences (U.S.)
2016
|
Online Access: | http://hdl.handle.net/1721.1/106154 https://orcid.org/0000-0003-1913-1396 https://orcid.org/0000-0002-4326-7720 |
Similar Items
-
Time-domain diffuse correlation spectroscopy: instrument prototype, preliminary measurements, and theoretical modeling
by: Tyulmankov, Danil
Published: (2018) -
Chronic Stress Alters Striosome-Circuit Dynamics, Leading to Aberrant Decision-Making
by: Friedman, Alexander, et al.
Published: (2020) -
A multistage mathematical approach to automated clustering of high-dimensional noisy data
by: Friedman, Alexander, et al.
Published: (2015) -
Meta-learning synaptic plasticity and memory addressing for continual familiarity detection
by: Tyulmankov, Danil, et al.
Published: (2023) -
Shifting Responsibly: The Importance of Striatal Modularity to Reinforcement Learning in Uncertain Environments
by: Ken-ichi eAmemori, et al.
Published: (2011-05-01)