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Humans and animals can recognize latent structures in their environment and apply this information to efficiently navigate the world.
Three unfinished works on the optimal storage capacity of networks
Elizabeth Gardner and Bernard Derrida · 1989
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Towards a theory of early visual processing
Joseph J Atick and A Norman Redlich · 1990
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Statistical Mechanics of Learning
A Engel and C Van den Broeck · 2005
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Neural correlations, population coding and computation
Bruno B Averbeck, Peter E Latham, and Alexandre Pouget · 2006
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Place cells, grid cells, and the brain’s spatial representation system
Edvard I Moser, Emilio Kropff, and May-Britt Moser · 2008
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Attention improves performance primarily by reducing interneuronal correlations
Marlene R Cohen and John HR Maunsell · 2009
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Efficient coding of spatial information in the primate retina
Eizaburo Doi, Jeffrey L Gauthier, Greg D Field, Jonathon Shlens, Alexander Sher, Martin Greschner, Timothy A Machado, Lauren H Jepson, Keith Mathieson, Deborah E Gunning, et al · 2012
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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Efficient sensory encoding and bayesian inference with heterogeneous neural populations
Deep Ganguli and Eero P Simoncelli · 2014
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Sparseness and expansion in sensory representations
Baktash Babadi and Haim Sompolinsky · 2014
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Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance
Najib J Majaj, Ha Hong, Ethan A Solomon, and James J DiCarlo · 2015
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Organizing conceptual knowledge in humans with a gridlike code
Alexandra O. Constantinescu, Jill X. O’Reilly, and Timothy E. J. Behrens · 2016
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Reorganization between preparatory and movement population responses in motor cortex
Gamaleldin F Elsayed, Antonio H Lara, Matthew T Kaufman, Mark M Churchland, and John P Cunningham · 2016
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Explicit information for category-orthogonal object properties increases along the ventral stream
Ha Hong, Daniel LK Yamins, Najib J Majaj, and James J DiCarlo · 2016
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The code for facial identity in the primate brain
Le Chang and Doris Y Tsao · 2017
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Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit
Dmitriy Aronov, Rhino Nevers, and David W. Tank · 2017
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dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
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The hippocampus as a predictive map
Kimberly L Stachenfeld, Matthew M Botvinick, and Samuel J Gershman · 2017
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The successor representation in human reinforcement learning
Ida Momennejad, Evan M Russek, Jin H Cheong, Matthew M Botvinick, Nathaniel Douglass Daw, and Samuel J Gershman · 2017
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Optimal degrees of synaptic connectivity
Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, and LF Abbott · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Towards a definition of disentangled representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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What is a cognitive map? organizing knowledge for flexible behavior
Timothy EJ Behrens, Timothy H Muller, James CR Whittington, Shirley Mark, Alon B Baram, Kimberly L Stachenfeld, and Zeb Kurth-Nelson · 2018
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Classification and geometry of general perceptual manifolds
SueYeon Chung, Daniel D Lee, and Haim Sompolinsky · 2018
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Motor cortex embeds muscle-like commands in an untangled population response
Abigail A Russo, Sean R Bittner, Sean M Perkins, Jeffrey S Seely, Brian M London, Antonio H Lara, Andrew Miri, Najja J Marshall, Adam Kohn, Thomas M Jessell, et al · 2018
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A neural population mechanism for rapid learning
Matthew G Perich, Juan A Gallego, and Lee E Miller · 2018
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Learning and attention reveal a general relationship between population activity and behavior
Amy M Ni, Douglas A Ruff, Joshua J Alberts, Jen Symmonds, and Marlene R Cohen · 2018
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Efficiency of learning vs. processing: Towards a normative theory of multitasking
Yotam Sagiv, Sebastian Musslick, Yael Niv, and Jonathan Cohen · 2018
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Grid-like Neural Representations Support Olfactory Navigation of a Two-Dimensional Odor Space
Xiaojun Bao, Eva Gjorgieva, Laura K. Shanahan, James D. Howard, Thorsten Kahnt, and Jay A. Gottfried · 2019
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High-dimensional geometry of population responses in visual cortex
Carsen Stringer, Marius Pachitariu, Nicholas Steinmetz, Matteo Carandini, and Kenneth D Harris · 2019
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Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks
Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan · 2021
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Orthogonal representations for robust context-dependent task performance in brains and neural networks
Timo Flesch, Keno Juechems, Tsvetomira Dumbalska, Andrew Saxe, and Christopher Summerfield · 2022
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Large-scale neural recordings call for new insights to link brain and behavior
Anne E Urai, Brent Doiron, Andrew M Leifer, and Anne K Churchland · 2022
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Neural representational geometry underlies few-shot concept learning
Ben Sorscher, Surya Ganguli, and Haim Sompolinsky · 2022
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The gaussian equivalence of generative models for learning with shallow neural networks
Sebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala, Marc Mézard, and Lenka Zdeborová · 2022
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Task representations in neural networks trained to perform many cognitive tasks
Guangyu Robert Yang, Madhura R Joglekar, H Francis Song, William T Newsome, and Xiao-Jing Wang · 2019
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A mathematical theory of semantic development in deep neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2019
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The geometry of abstraction in the hippocampus and prefrontal cortex
Silvia Bernardi, Marcus K Benna, Mattia Rigotti, Jérôme Munuera, Stefano Fusi, and C Daniel Salzman · 2020
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Separability and geometry of object manifolds in deep neural networks
Uri Cohen, SueYeon Chung, Daniel D Lee, and Haim Sompolinsky · 2020
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Object manifold geometry across the mouse cortical visual hierarchy
Emmanouil Froudarakis, Uri Cohen, Maria Diamantaki, Edgar Y Walker, Jacob Reimer, Philipp Berens, Haim Sompolinsky, and Andreas S Tolias · 2020
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Modeling the influence of data structure on learning in neural networks: The hidden manifold model
Sebastian Goldt, Marc Mézard, Florent Krzakala, and Lenka Zdeborová · 2020
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Information-limiting correlations in large neural populations
Ramon Bartolo, Richard C Saunders, Andrew R Mitz, and Bruno B Averbeck · 2020
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Origami in n dimensions: How feed-forward networks manufacture linear separability, 2022
Christian Keup and Moritz Helias · 2022
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Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Laura Driscoll, Krishna Shenoy, and David Sussillo · 2022
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Population codes enable learning from few examples by shaping inductive bias
Blake Bordelon and Cengiz Pehlevan · 2022
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Abstract representations emerge in human hippocampal neurons during inference behavior
Hristos S. Courellis, Juri Mixha, Araceli R. Cardenas, Daniel Kimmel, Chrystal M. Reed, Taufik A. Valiante, C. Daniel Salzman, Adam N. Mamelak, Stefano Fusi, and Ueli Rutishauser · 2023
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The geometry of cortical representations of touch in rodents
Ramon Nogueira, Chris C Rodgers, Randy M Bruno, and Stefano Fusi · 2023
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Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization
W Jeffrey Johnston, Justin M Fine, Seng Bum Michael Yoo, R Becket Ebitz, and Benjamin Y Hayden · 2023
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Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W Jeffrey Johnston and Stefano Fusi · 2023
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Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex
Paul S Muhle-Karbe, Hannah Sheahan, Giovanni Pezzulo, Hugo J Spiers, Samson Chien, Nicolas W Schuck, and Christopher Summerfield · 2023
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Linear classification of neural manifolds with correlated variability
Albert J Wakhloo, Tamara J Sussman, and SueYeon Chung · 2023
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Universality of max-margin classifiers
Andrea Montanari, Feng Ruan, Basil Saeed, and Youngtak Sohn · 2023
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Factorized visual representations in the primate visual system and deep neural networks
Jack W Lindsey and Elias B Issa · 2023
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Neural learning rules for generating flexible predictions and computing the successor representation
Ching Fang, Dmitriy Aronov, LF Abbott, and Emily L Mackevicius · 2023
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A unified theory for the computational and mechanistic origins of grid cells
Ben Sorscher, Gabriel C Mel, Samuel A Ocko, Lisa M Giocomo, and Surya Ganguli · 2023
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Task-dependent optimal representations for cerebellar learning
Marjorie Xie, Samuel P Muscinelli, Kameron Decker Harris, and Ashok Litwin-Kumar · 2023
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Optimal routing to cerebellum-like structures
Samuel P Muscinelli, Mark J Wagner, and Ashok Litwin-Kumar · 2023
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A spectral theory of neural prediction and alignment
Abdulkadir Canatar, Jenelle Feather, Albert J Wakhloo, and SueYeon Chung · 2023
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Orthogonal neural representations support perceptual judgements of natural stimuli
Ramanujan Srinath, Amy M Ni, Claire Marucci, Marlene R Cohen, and David H Brainard · 2024
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Neural manifold capacity captures representation geometry, correlations, and task-efficiency across species and behaviors
Chi-Ning Chou, Luke Arend, Albert J Wakhloo, Royoung Kim, Will Slatton, and SueYeon Chung · 2024
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Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number
Jason Manley, Sihao Lu, Kevin Barber, Jeffrey Demas, Hyewon Kim, David Meyer, Francisca Martínez Traub, and Alipasha Vaziri · 2024
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