A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex
Open accessAbstract
Medial entorhinal grid cells display strikingly symmetric spatial firing patterns. The clarity of these patterns motivated the use of specific activity pattern shapes to classify entorhinal cell types. While this approach successfully revealed cells that encode boundaries, head direction, and running speed, it left a majority of cells unclassified, and its pre-defined nature may have missed unconventional, yet important coding properties. Here, we apply an unbiased statistical approach to search for cells that encode navigationally relevant variables. This approach successfully classifies the majority of entorhinal cells and reveals unsuspected entorhinal coding principles. First, we find a high degree of mixed selectivity and heterogeneity in superficial entorhinal neurons. Second, we discover a dynamic and remarkably adaptive code for space that enables entorhinal cells to rapidly encode navigational information accurately at high running speeds. Combined, these observations advance our current understanding of the mechanistic origins and functional implications of the entorhinal code for navigation. VIDEO ABSTRACT.
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Remapping comprised discrete transitions between aligned neural activity manifolds, each representing a distinct map of an unchanging virtual reality 1D environment.
“Remapping comprised discrete transitions between aligned neural activity manifolds, which each represented a distinct map of an unchanging, virtual reality 1D environment (Figure 1C-D).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
Remapping was correlated with transient decreases in running speed, which could correspond to discrete changes in latent state.
“Remapping was correlated with transient decreases in running speed (Figure 1B), which could correspond to discrete changes in latent state (such as shifts in arousal, task engagement, or other behavioral states).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
These task constraints are sufficient to organize randomly initialized synaptic weights into a network model that qualitatively reproduces the remapping dynamics and representational geometry observed in the entorhinal cortex.
“Thus, these task constraints are sufficient to organize randomly initialized synaptic weights into a network model that qualitatively reproduces the remapping dynamics and representational geometry that Low et al. observed in the entorhinal cortex.26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
Procrustes shape analysis demonstrated that the rings were more aligned in high-dimensional activity space than expected by chance for all trained models, such that position along one ring matched position on the second ring.
“As in Low et al., 26 we used Procrustes shape analysis to demonstrate that these rings were more aligned in high-dimensional activity space than expected by chance for all trained models, such that the position along one ring matched the position on the second ring (Figure 1J).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
A new analysis of experimental data from Low et al. found a similar modular structure in neural activity from a subset of sessions with more than two stable spatial maps.
“Furthermore, we performed a new analysis of experimental data published in Low et al. 26 and found a similar modular structure in neural activity from a subset of sessions with more than two stable spatial maps.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
RNNs trained to navigate while inferring latent state changes exhibited network-wide activity patterns strikingly similar to those found in the brain.
“We found that RNNs trained to navigate while inferring latent state changes exhibited network-wide activity patterns that were strikingly similar to those found in the brain, 26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
Low et al. demonstrated that remapping in the medial entorhinal cortex simultaneously recruited large populations of neurons across the entorhinal cortical circuit.
“In this study, Low et al. demonstrated that remapping in the medial entorhinal cortex simultaneously recruited large populations of neurons across the entorhinal cortical circuit.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
This modular motif also emerged in the novel analysis of neural data from Low et al., suggesting that biological systems may also leverage this strategy.
“Intriguingly, this modular motif also emerged in our novel analysis of neural data from Low et al., 26 suggesting that biological systems may also leverage this strategy.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
Neither the RNN nor the biological data are consistent with the simplest 3-dimensional solution, as evidenced by imperfect ring alignment, variable remapping vectors, and the dimensionality of the network.
“Notably, neither the RNN nor the biological data are consistent with the simplest 3-dimensional solution, as evidenced by the imperfect ring alignment (Low et al. 26 and Figure 1J), the variable remapping vectors (Fig. 2C-D), and the dimensionality of the network (which is 3; Figure 1I).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
In experimental sessions from Low et al. that exhibited remapping between 3-4 stable maps, remapping events did not reflect recording probe movement; waveforms from different maps across the session were highly stable.
“As in Low et al. 26 we first confirmed that these remapping events did not reflect recording probe movement by comparing the waveforms from different maps across the session, which were highly stable (Figure 6B, right; Supplemental Figure 4).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
Low et al. most commonly observed remapping between two maps of the same virtual track, and occasionally found transitions between more than two maps.
“Indeed, while Low et al. most commonly observed remapping between two maps of the same virtual track, they occasionally found transitions between more than two maps.26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
These findings provide a jumping off point for new analyses of remapping in neural data, demonstrated in a pilot analysis of Low et al.
“These findings provide a jumping off point for new analyses of remapping in neural data, which we demonstrated in a pilot analysis of neural data from Low et al. 26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
In Low et al., remapping was correlated with changes in running speed and position was preserved across remapping events.
“In particular, remapping was correlated with changes in running speed and position was preserved across remapping events.26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
This simpler solution recapitulates the geometry of entorhinal cortical dynamics during remapping in virtual reality environments.
“This simpler solution recapitulates the geometry of entorhinal cortical dynamics during remapping in virtual reality environments.26”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
RNNs initialized from random synaptic weights recapitulate the essential features of biological data, aligned ring manifolds, when trained to remember a binary latent state variable and to simultaneously integrate a velocity signal in a circular environment.
“We showed that RNNs initialized from random synaptic weights recapitulate the essential features of biological data-aligned ring manifolds 26 -when trained to remember a binary latent state variable and to simultaneously integrate a velocity signal in a circular environment.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [26]
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Self-citation
- Authors
- Hardcastle, K., Maheswaranathan, N., Ganguli, S., & Giocomo, L. M.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Hardcastle, Kiah
- Giocomo, Lisa M.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 94
- Issue
- 2
- Pages
- 375-387.e7
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC5498174
- OpenAlex
- W2605235639
- Cited by
- 312
- References
- 71
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 36 · 2023: 42 · 2022: 33 · 2021: 31 · 2020: 30 · 2019: 42 · 2018: 32 · 2017: 8
- FWCI
- 17.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- The New York Stem Cell Foundation
- James S. McDonnell Foundation
- NIMH (MH106475)
- Klingenstein-Simons Fellowship
- Bio-X Interdisciplinary Initiatives Program
- Simons Foundation
- Burroughs-Wellcome
- Alfred P. Sloan Foundation
- McKnight Foundation
- Office of Naval Research (N00014-16-1-2206)
- NSF-IGERT from the Stanford MBC Program
- Stanford Interdisciplinary Graduate Fellowship
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- medial entorhinal cortex, grid cells, spatial coding, mixed selectivity, neuronal heterogeneity, navigation, head direction, running speed, boundary cells, hippocampal formation
- MeSH terms
- Action Potentials, Animals, Female, Head, Male, Mice, Inbred C57BL, Models, Neurological, Motor Activity, Neurons, Space Perception, Theta Rhythm, Entorhinal Cortex
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