Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces DOI Creative Commons
Chethan Pandarinath, K. Cora Ames, Abigail A. Russo

и другие.

Journal of Neuroscience, Год журнала: 2018, Номер 38(44), С. 9390 - 9401

Опубликована: Окт. 31, 2018

In the 1960s, Evarts first recorded activity of single neurons in motor cortex behaving monkeys (Evarts, 1968). 50 years since, great effort has been devoted to understanding how neuron relates movement. Yet these exist within a vast network, nature which largely inaccessible. With advances recording technologies, algorithms, and computational power, ability study networks is increasing exponentially. Recent experimental results suggest that dynamical properties are critical movement planning execution. Here we discuss this systems perspective it reshaping our cortices. Following an overview key studies cortex, techniques uncover "latent factors" underlying observed neural population activity. Finally, efforts use factors improve performance brain-machine interfaces, promising make findings broadly relevant neuroengineering as well neuroscience.

Язык: Английский

Computation Through Neural Population Dynamics DOI
Saurabh Vyas, Matthew D. Golub, David Sussillo

и другие.

Annual Review of Neuroscience, Год журнала: 2020, Номер 43(1), С. 249 - 275

Опубликована: Июль 8, 2020

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover nature associated computations, how they are implemented, what role play in driving behavior. We term this computation through population dynamics. If successful, framework will reveal general motifs quantitatively describe dynamics implement computations necessary for goal-directed Here, we start with a mathematical primer on dynamical systems theory analytical tools apply perspective experimental data. Next, highlight some recent discoveries resulting from successful application systems. focus studies spanning motor control, timing, decision-making, working memory. Finally, briefly discuss promising lines investigation future directions framework.

Язык: Английский

Процитировано

535

If deep learning is the answer, what is the question? DOI
Andrew Saxe, Stephanie Nelli, Christopher Summerfield

и другие.

Nature reviews. Neuroscience, Год журнала: 2020, Номер 22(1), С. 55 - 67

Опубликована: Ноя. 16, 2020

Язык: Английский

Процитировано

344

Long-term stability of cortical population dynamics underlying consistent behavior DOI
Juan A. Gallego, Matthew G. Perich, Raeed H. Chowdhury

и другие.

Nature Neuroscience, Год журнала: 2020, Номер 23(2), С. 260 - 270

Опубликована: Янв. 6, 2020

Язык: Английский

Процитировано

342

Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks DOI Creative Commons
Francesca Mastrogiuseppe, Srdjan Ostojic

Neuron, Год журнала: 2018, Номер 99(3), С. 609 - 623.e29

Опубликована: Июль 26, 2018

Язык: Английский

Процитировано

333

Accurate Estimation of Neural Population Dynamics without Spike Sorting DOI Creative Commons
Eric M. Trautmann, Sergey D. Stavisky, Subhaneil Lahiri

и другие.

Neuron, Год журнала: 2019, Номер 103(2), С. 292 - 308.e4

Опубликована: Июнь 3, 2019

Язык: Английский

Процитировано

242

Bayesian Computation through Cortical Latent Dynamics DOI Creative Commons
Hansem Sohn, Devika Narain, Nicolas Meirhaeghe

и другие.

Neuron, Год журнала: 2019, Номер 103(5), С. 934 - 947.e5

Опубликована: Июль 15, 2019

Язык: Английский

Процитировано

224

Re-evaluating Circuit Mechanisms Underlying Pattern Separation DOI Creative Commons
N. Alex Cayco-Gajic, R. Angus Silver

Neuron, Год журнала: 2019, Номер 101(4), С. 584 - 602

Опубликована: Фев. 1, 2019

Язык: Английский

Процитировано

203

A Neural Population Mechanism for Rapid Learning DOI Creative Commons
Matthew G. Perich, Juan A. Gallego, Lee E. Miller

и другие.

Neuron, Год журнала: 2018, Номер 100(4), С. 964 - 976.e7

Опубликована: Окт. 18, 2018

Язык: Английский

Процитировано

180

Hierarchical reasoning by neural circuits in the frontal cortex DOI Open Access

Morteza Sarafyazd,

Mehrdad Jazayeri

Science, Год журнала: 2019, Номер 364(6441)

Опубликована: Май 16, 2019

Humans process information hierarchically. In the presence of hierarchies, sources failures are ambiguous. resolve this ambiguity by assessing their confidence after one or more attempts. To understand neural basis reasoning strategy, we recorded from dorsomedial frontal cortex (DMFC) and anterior cingulate (ACC) monkeys in a task which negative outcomes were caused either misjudging stimulus covert switch between two stimulus-response contingency rules. We found that both areas harbored representation evidence supporting rule switch. Additional perturbation experiments revealed ACC functioned downstream DMFC was directly specifically involved inferring switches. These results‏ reveal computational principles hierarchical reasoning, as implemented cortical circuits.

Язык: Английский

Процитировано

167

The role of population structure in computations through neural dynamics DOI
Alexis Dubreuil, Adrian Valente, Manuel Beirán

и другие.

Nature Neuroscience, Год журнала: 2022, Номер 25(6), С. 783 - 794

Опубликована: Июнь 1, 2022

Язык: Английский

Процитировано

154