Specific evidence of low-dimensional continuous attractor dynamics in grid cells

Yoon, KiJung, Buice, Michael A, Barry, Caswell, Hayman, Robin, Burgess, Neil, Fiete, Ila R·2013·Nature Neuroscience
DOI 10.1038/nn.3450

Cited by (272)

Items 201–272 of 272

  1. Efficient low-dimensional approximation of continuous attractor networks
    Alexander Seeholzer, Moritz Deger, Wulfram Gerstner B · 2017 · arXiv (Cornell University)
  2. Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals
    Abhiram Iyer, Sarthak Chandra, Sugandha Sharma, I. Fiete · 2024 · Neural Information Processing Systems
  3. Continuous attractor dynamics in spatial navigation: from population geometry to flexible computation
    Y. L. Chen, Mu Hua, Xuelong Sun, Jigen Peng · 2026 · Frontiers in Neuroscience
  4. A Comprehensive Review of Brain-inspired Navigation
    Xu He, Xiaolin Meng, Lingfei Mo, Youdong Zhang, Fangwen Yu, Jingnan Liu · 2025 · ACM Computing Surveys
  5. A memory model of rodent spatial navigation in which place cells are memories arranged in a grid and grid cells are non-spatial
    David E. Huber · 2024 · eLife
  6. A theory and recipe to construct general and biologically plausible integrating continuous attractor neural networks
    Federico Claudi, Sarthak Chandra, Ila Fiete · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  7. Flexibility of memory for future-oriented cognition
    Kaori Takehara‐Nishiuchi · 2022 · Current Opinion in Neurobiology
  8. Emergence of robust global modules from local interactions and smooth gradients
    Mikail Khona, Sarthak Chandra, I. Fiete · 2021 · bioRxiv
  9. Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
    Jason Z. Kim, Zhi-Xin Lu, Erfan Nozari, George Pappas, D. Bassett · 2020 · arXiv.org
  10. Analysis of an Attractor Neural Network’s Response to Conflicting External Inputs
    Kathryn R. Hedrick, Kechen Zhang · 2018 · The Journal of Mathematical Neuroscience
  11. Navigate the Unknown: Implications of Grid-Cells “Mental Travel” in Vicarious Trial and Error
    Diogo Santos-Pata, Riccardo Zucca, Paul F. M. J. Verschure · 2016 · Lecture notes in computer science
  12. The recipe for intelligence in natural and artificial systems
    Christopher Summerfield, Kimberly Stachenfeld · 2026 · Neuron
  13. A Two-Dimensional Grid-Cell Code for Three-Dimensional Navigation in Freely Flying Bats
    Kevin K. Qi, Michael M. Yartsev · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  14. Minute‐Scale Oscillations in Sparse Neural Networks
    José A. Fernández-León · 2025 · Hippocampus
  15. Synaptic interactions between stellate cells and parvalbumin interneurons in layer 2 of the medial entorhinal cortex are organized at the scale of grid cell clusters
    Li-Wen Huang, Derek L. F. Garden, Christina McClure, Matthew F Nolan · 2024 · bioRxiv
  16. Medial entorhinal cortex plays a specialized role in learning of flexible, context-dependent interval timing behavior
    Erin R. Bigus, Hyun-Woo Lee, Jiani Shi, James G. Heys · 2023 · Research Square
  17. Weight matrix as a switch between line attractor and plane attractor of ring neural networks
    Jiali Yu, Wenshuang Chen, Jinsong Leng, Chunxiao Wang, Yi Zhang · 2022 · Neurocomputing
  18. Vector production via mental navigation in the entorhinal cortex
    Sujaya Neupane, Ila Fiete, Mehrdad Jazayeri · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
  19. The effect of boundaries on grid cell patterns
    Mauro M. Monsalve-Mercado, C. Leibold · 2020 · bioRxiv
  20. Modulating grid cell scale and intrinsic frequencies via slow high-threshold conductances: A simplified model
    Diogo Santos-Pata, Riccardo Zucca, Héctor López-Carral, Paul F. M. J. Verschure · 2019 · Neural Networks
  21. An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co‐firing rates of neural spike trains
    Ryan Grgurich, Hugh T. Blair · 2020 · Hippocampus
  22. Velocity coupling of grid cell modules: stable embedding of a low dimensional variable in a high dimensional neural attractor
    Noga Mosheiff, Y. Burak · 2019 · bioRxiv
  23. Models of spatial representation in the medial entorhinal cortex
    Tiziano D’Albis · 2018
  24. Comparative Effects of Span 20 and Span 40 on Liposomes Release Properties
    Radoslava N. Pravilović, Bojana D. Balanč, K. Trifković, V. Đorđević, N. Bošković-Vragolović, B. Bugarski, R. Pjanovic · 2017
  25. Speed tuning in the macaque posterior parietal cortex
    Eric Avila, Kaushik J. Lakshminarasimhan, G. DeAngelis, D. Angelaki · 2017 · bioRxiv
  26. Velocity Integration in a Multilayer Neural Field Model of Spatial Working Memory
    Daniel B Poll, Zachary P. Kilpatrick · 2017 · SIAM Journal on Applied Dynamical Systems
  27. Disrupting the Grid Cells’ Need for Speed
    Robin M. A. Hayman, Neil Burgess · 2016 · Neuron
  28. On the optimality of grid cells
    Christos H. Papadimitriou · 2016 · arXiv (Cornell University)
  29. Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks
    Lukas Solanka, Mark C. W. van Rossum, Matthew F. Nolan · 2015 · eLife
  30. Symmetries and continuous attractors in disordered neural circuits
    David G. Clark, Larry F. Abbott, Haim Sompolinsky · 2026 · Nature Neuroscience
  31. Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
    Tian-Qiu Zhang, Muyang Lyu, Xiao Liu, Si Wu · 2026 · arXiv.org
  32. State-dependent spatial maps for navigation
    Lavonna Mark, Lisa Marie Giocomo · 2026 · Trends in Cognitive Sciences
  33. Mechanistic Integration of Distal and Proximal Cues in the Rodent Entorhinal–Hippocampal Circuit: Insights From a Biorobotics Model
    Y. L. Chen, Mu Hua, Ziyan Qin, Shigang Yue, Xuelong Sun, Jigen Peng · 2026 · European Journal of Neuroscience
  34. A parietal grid-like code rotates with cognitive maps but lags rapid behavioral transfer
    Linda Q. Yu, A. R. Vaidya, Aigerim (Aya) Akhmetzhanova, Sienna Bruinsma, Matthew R. Nassar · 2025 · bioRxiv
  35. Ground tilt representation in the rodent cerebral cortex
    Fumio Imamura, Hiroto Imamura, Reiko Hira, Yoshikazu Isomura, Riichiro Hira · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  36. Conditional multi-attractivity analysis of recurrent neural networks based on matrix decomposition
    Jialu Zuo, Shaoxin Tian, Wang Liao, Jiali Yu, Zhang Yi · 2025 · Communications in nonlinear science & numerical simulation
  37. A Grid Cell-Inspired Structured Vector Algebra for Cognitive Maps
    Sven Krausse, Emre Neftci, Friedrich T. Sommer, Alpha Renner · 2025 · Neuro Inspired Computational Elements Conference (NICE)
  38. Understanding and controlling the geometry of memory organization in RNNs
    Udith Haputhanthri, Liam Storan, Yi-Qi Jiang, Tarun Raheja, Adam Shai, Orhun Akengin, N. Miolane, Mark J. Schnitzer, Fatih Dinç, Hidenori Tanaka · 2025
  39. The synaptic correlates of serial position effects in sequential working memory
    Jiaqi Zhou, Liping Gong, Xiaodong Huang, Chunlai Mu, Yuanyuan Mi · 2024 · Frontiers in Computational Neuroscience
  40. Medial entorhinal cortex plays a specialized role in learning of flexible, context-dependent interval timing behavior
    Erin R. Bigus, Hyun‐Woo Lee, John C. Bowler, Jiani Shi, James G. Heys · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
  41. Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity.
    Tobias Kühn, R. Monasson · 2023 · Physical Review E
  42. Do place cells dream of deceptive moves in a signaling game?
    A. Fenton, J. R. Hurtado, Jantine A. C. Broek, EunHye Park, B. Mishra · 2022 · bioRxiv
  43. The Interplay between Grid and Place Cell Networks and Contextual Inputs
    Jiarui Wang · 2022 · International Conference on Bioinformatics and Intelligent Computing (BIC)
  44. Toward a Unified Framework for Cognitive Maps
    Woori Kim, Yongseok Yoo · 2020 · Neural Computation
  45. Where can a place cell put its fields? Let us count the ways
    Man-Yi Yim, L. Sadun, I. Fiete, Thibaud O. Taillefumier · 2019 · bioRxiv
  46. Grid pattern development, distortions and topological defects may depend on distributed anchoring
    Maria Mørreaunet, Martin Hägglund · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
  47. A Survey of Entorhinal Grid Cell Properties
    Jochen Kerdels, Gabriele Peters · 2018 · arXiv (Cornell University)
  48. Continuous attractor working memory and provenance of channel models
    Alexander Seeholzer · 2017
  49. Temporal instability weakens entorhinal grid-like coding in older adults
    Vladislava Segen, Gerard Callau Navarro, Philip Bahrd, Tugce Belge Bernard, Julia-Isabel Schinke, Thomas Wolbers · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  50. Core–periphery organization reveals functional heterogeneity in spatial coding ensembles
    Ruixin Qian, Tao Wang, Yunxiang Chen, Wei Wang, Feng Liu · 2026 · Biophysics Reviews
  51. Retrospective Grid Cells in the Medial Entorhinal Cortex Encode Past Paths During Spatial Navigation
    Ruojin Liu, Xin Yuan, Zilong He, Hong Jin, Jiayi Tian, Cong Chen, Yuanjing Liu, Xiang Zhang, Shidan Wen, Emilio Kropff et al. · 2026 · Advanced Science
  52. Brain-Inspired path integration based on multiscale Turn-Modulated CANN in martian environments
    Yixin Liu, Zeyu Wang, Lingling Wang, Li Jun Fu, Maria S. Selezneva, Konstantin A. Neusypin · 2026 · Expert Systems with Applications
  53. Structural generalization and continual learning enabled by factorized entorhinal-hippocampal memory and entorhinal-parietal action circuits
    Jaedong Hwang, Sujaya Neupane, Mehrdad Jazayeri, Ila Fiete · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  54. Spatial and temporal representations are organized along a stable coding gradient in the medial entorhinal cortex
    Hyun‐Woo Lee, John C. Bowler, James G. Heys · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  55. Computational demands shape seizure susceptibility in recurrent neural networks
    Muhang Li, Sebastian Eydam, Ismaeel Ramzan, Denis Polygalov, A. Huang, I. Taguas, Hannah Nemeth, Dai Yanagihara, T. McHugh, Louis Kang · 2026 · bioRxiv
  56. Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints
    Ruojin Liu, Jun Hao, Xiang Zhang, Shidan Wen, Youran Yang, Haiqian Cai, Kai Gao, Emilio Kropff, Chenglin Miao · 2026 · Nature Communications
  57. Computational modeling of head direction cells in three-dimensional space: directional encoding and visual cue manipulation
    Yihong Wang, Jian-Biao Hu, Shuang Xu, Xuying Xu, Xiao-Chuan Pan, Ru-Bin Wang · 2026 · bioRxiv
  58. Self-sustained and oscillatory activity in two types of attractor networks
    Tao Wang, Jun-Xiang Gui, Mengjiao Zuo, Wei Wang, Feng Liu · 2025 · Nonlinear Dynamics
  59. Robust variability of grid cell properties within individual grid modules enhances encoding of local space
    William T. Redman, Santiago Acosta–Mendoza, Xue-Xin Wei, Michael J. Goard · 2024 · eLife
  60. Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration
    Gorkem Secer, James J. Knierim, Noah J. Cowan · 2024 · Research Square
  61. Grid Cell Firing Patterns Maintain their Hexagonal Firing Patterns on a Circular Track
    Man Yi Yim, Steven Walton, Kathryn R. Hedrick · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
  62. Hyper-alignment: Great mice think alike
    Federico Stella, Alessandro Treves · 2021 · Current Biology
  63. Optimizations on unknown low-dimensional structures given by high-dimensional data
    Qi-Li Chen, Jiu-He Wang, Junfei Qiao, Ming-Yi Zou · 2021 · Soft Computing - A Fusion of Foundations, Methodologies and Applications
  64. Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons
    Christian Huyck, Alberto Arturo Vergani · 2020 · Journal of Computational Neuroscience
  65. A simplified spiking model of grid-cell scale and intrinsic frequencies
    D. Santos-Pata, R. Zucca, Héctor López-Carral, P. Verschure · 2019 · bioRxiv
  66. Continuous Attractors of Nonlinear Neural Networks with Asymmetric Connection Weights
    Jiali Yu, Zhang Yi, Chun-Xiao Wang, Yong Liao, Zhi-Xin Pang · 2018 · International Conference on Neural Information Processing
  67. Continuous Attractors of 3-D Discrete-Time Ring Networks with Circulant Weight Matrix
    Jiali Yu, Yi Zhang, Yong Liao, De-An Wu, Xiong Dai · 2018 · Lecture notes in computer science
  68. Multi-Transition Systems: A theory for neural spatial navigation
    Nicolai Waniek · 2017 · bioRxiv (Cold Spring Harbor Laboratory)
  69. Dimensionality Mismatch Between Brains and Artificial Neural Networks
    Santiago Galella, Maren H. Wehrheim, Matthias Kaschube · 2025 · Neural Information Processing Systems
  70. Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems
    Acosta, Francisco, Dinc, Fatih, Redman, William, Madhav, Manu, Klindt, David, Miolane, Nina · 2024 · Advances in Neural Information Processing Systems 37
  71. Untitled work
    Schøyen · 2022 · 10.1101/2022.08.18.504379
  72. Key-value memory in the brain
    Gershman, Samuel J., Fiete, Ila, Irie, Kazuki · 2025 · arXiv