The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
By Rishidev Chaudhuri, Berk Gerçek, Biraj Pandey, Adrien Peyrache, Ila Rani Fiete. 2019
362 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- David Tingley
- David Sussillo
- Saurabh Vyas
- Marco Tripodi
- Ta-Chu Kao
- Ida Momennejad
- Krishna V. Shenoy
- Adrien Peyrache
In Cognitive Neuroscience
- Guillaume Hennequin
- Krishna V. Shenoy
- Ta-Chu Kao
- Kristopher T. Jensen
- Saurabh Vyas
- Matthew D. Golub
- David Sussillo
- David Tingley
Citations in the corpus
- Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “An alternative theory states that neural representations are mixed selective but structured on a low-dimensional and task-specific manifold (Ganguli et al., 2008; Sadtler et al., 2014; Gao and Ganguli, 2015; Chaudhuri et al., 2019; Cueva et al., 2020) where correlated patterns of firing confer robus”
- Learning Structures: Predictive Representations, Replay, and Generalization (2020, background): “Various theoretical proposals have attempted to solve the puzzle with computational frameworks such as manifolds and neural networks [19,20], topological models [21,22], relational learning [23], and reinforcement learning [15 ].”
- Computation Through Neural Population Dynamics (2020, background): “A number of neuroscientific studies have derived insights into neural systems by leveraging manifold structures identified from neurophysiological data (e.g., Chaudhuri et al. 2019, Kim et al. 2017, Sadtler et al. 2014; for a review, see Gallego et al. 2017).”
- Manifold GPLVMs for discovering non-Euclidean latent structure in neural data (2020, background): “We expect mGPLVM to be particularly valuable to the neuroscience community because many quantities encoded in the brain naturally live in non-Euclidean spaces (Chaudhuri et al., 2019; Finkelstein et al., 2015; Wilson et al., 2018).”
- On the methods for reactivation and replay analysis (2020, methodology): “[43] neuronal excitability, SWRs Buzsaki [1,8] Pavlides & Winson [17]”