Specific evidence of low-dimensional continuous attractor dynamics in grid cells
Open accessAbstract
We examined simultaneously recorded spikes from multiple rat grid cells, to explain mechanisms underlying their activity. Among grid cells with similar spatial periods, the population activity was confined to lie close to a two-dimensional (2D) manifold: grid cells differed only along two dimensions of their responses and otherwise were nearly identical. Relationships between cell pairs were conserved despite extensive deformations of single-neuron responses. Results from novel environments suggest such structure is not inherited from hippocampal or external sensory inputs. Across conditions, cell-cell relationships are better conserved than responses of single cells. Finally, the system is continually subject to perturbations that, were the 2D manifold not attractive, would drive the system to inhabit a different region of state space than observed. These findings have strong implications for theories of grid-cell activity and substantiate the general hypothesis that the brain computes using low-dimensional continuous attractors.
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Low-dimensional attractors have been proposed to underlie a large range of neural computations, from storage of working memory to reduction of noise in sensory and cognitive representations.
“Low-dimensional attractors have been proposed to underlie a large range of neural computations, from storage of working memory to reduction of noise in sensory and cognitive representations16,17.”
From Predicting modular functions and neural coding of behavior from a synaptic wiring diagram (2024) · cites this paper as [17]
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BackgroundLearning place cells, grid cells and invariances with excitatory and inhibitory plasticity
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“Moreover, the coupling between anatomically nearby grid cells – e.g., the difference in spatial phase – is more stable to changes of the environment than the firing pattern of individual grid cells (Yoon et al., 2013).”
BackgroundIntegration of grid maps in merged environments
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“…such models are limited in their explanatory scope, hand-designed to capture the properties of one stereotypical cell-type class (e.g. grid or border cells) at a time, or combinations of several cell types via multiple dedicated type-specific modules [Couey et al., 2013, Yoon et al., 2013].”
BackgroundA Brainstem Locomotor Circuit Drives the Activity of Speed Cells in the Medial Entorhinal Cortex
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“Because the relative position of firing fields of different grid cells is maintained across environments and behavioral tasks (Fyhn et al., 2007; Yoon et al., 2013), self-motion, rather than external sensory inputs, may determine grid cell firing in moving animals (McNaughton et al.”
BackgroundDynamic and reversible remapping of network representations in an unchanging environmentPreprint
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BackgroundA geometric attractor mechanism for self-organization of entorhinal grid modules
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BackgroundHow environment and self‐motion combine in neural representations of space
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BackgroundSelf-citationEpisodic and associative memory from spatial scaffolds in the hippocampus
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“However, the whole of Vector-HaSH, working in the regime of recall of a familiar sequence in the memory continuum, can itself be converted into a new scaffold for precise memory, as follows.”
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“Such long-lasting correlations have been reported for populations of grid cells [33], and of head-direction cells across waking and sleep [34], populations of the Aplysia motor system across an hour or more [15], and for spontaneous activity in primary auditory cortex [35, 36].”
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“Attractor models were extended to grid cells based on two-dimensional circularly symmetric inhibitory interactions between neurons, which result in a pattern of “bumps” of activity (Burak and Fiete 2009; Conklin and Eliasmith 2005; Fuhs and Touretzky 2006; Giocomo et al. 2011; McNaughton et al. 2006; Widloski and Fiet…”
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“, 2012) that shift together during environmental manipulations (Barry, Hayman, Burgess, & Jeffery, 2007; Yoon et al., 2013).”
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“Furthermore, the fields of different grid cells move and rotate in concert across different environments (Fyhn et al., 2007; Yoon et al., 2013), or when fields drift in the absence of sensory cues (Waaga et al., 2022), suggesting that grid cells underlie a coherent and reusable metric…”
BackgroundObject-centered population coding in CA1 of the hippocampus
Nagelhus, A., Andersson, S. O., Cogno, S. G., Moser, E., & Moser, M. (2022) · bioRxiv · doi:10.1101/2022.07.07.499197 · Cited by 35
- Authors
- Yoon, K., Buice, M. A., Barry, C., Hayman, R., Burgess, N., & Fiete, I. R.
- Affiliations
- The University of Texas at Austin (US)
- UCL Queen Square Institute of Neurology (GB)
- University College London (GB)
- Corresponding author
- Fiete, Ila R
- Venue
- Nature Neuroscience
- Published
- 2013-07-14
- Volume
- 16
- Issue
- 8
- Pages
- 1077-1084
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3797513
- OpenAlex
- W2022401803
- Cited by
- 296
- References
- 51
- Citations by year
- 2026: 16 · 2025: 28 · 2024: 36 · 2023: 26 · 2022: 18 · 2021: 26 · 2020: 24 · 2019: 28 · 2018: 19 · 2017: 23 · 2016: 15 · 2015: 17 · 2014: 15 · 2013: 5
- FWCI
- 9.51
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (095811)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- grid cells, low-dimensional attractors, spatial coding, robustness to disturbances, rat hippocampus, spatial representation, attractor networks
- MeSH terms
- Action Potentials, Algorithms, Animals, Computer Simulation, Exploratory Behavior, Models, Neurological, Nerve Net, Neurons, Pattern Recognition, Visual, Space Perception, Spatial Behavior, Neural Networks, Computer, Patch-Clamp Techniques, Entorhinal Cortex, Rats
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