A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Levy, Eliott Robert Joseph; Carrillo-Segura, Simón; Park, Eun Hye; Redman, William Thomas; Hurtado, José Rafael; Chung, SueYeon; Fenton, André Antonio

doi:10.1016/j.celrep.2023.113142

Cited by 10 publications

(7 citation statements)

References 129 publications

(174 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1), and thus the reported changes in phase may be sufficient to generate large (and seemingly random) changes in local spatial information conveyed by grid cells to hippocampal cells. This could drive additional changes in the local spatial information projected to hippocampus, as well as explain the significant differences in correlation structure between CA1 neurons across different environments [64].…”

Section: Discussionmentioning

confidence: 99%

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Redman,

Acosta-Mendoza,

Wei

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Although grid cells have become one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per module has remained untested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties, within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Redman,

Acosta-Mendoza,

Wei

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our work is consistent with previous proposals that suggest a primary function of the hippocampus is to generate sequential activity at a baseline state 17,27,29,47,48 . These sequences could then act as a substrate that can become anchored to particular salient reference points 16,17,27,47 , allowing maximal flexibility to learn new information as it becomes relevant and layer it onto existing representations 159–161 . This role for hippocampal sequences extends to computational studies 28,159,162 and human research 163–167 , where such coding schemes have been linked to the organization of knowledge and the chronological order of events in episodic memory 27,49,163–166,168–170 .…”

Section: Discussionmentioning

confidence: 99%

Hippocampal sequences span experience relative to rewards

Sosa,

Plitt,

Giocomo

2023

Preprint

View full text Add to dashboard Cite

Hippocampal place cells fire in sequences that span spatial environments, and remap, or change their preferred firing locations, across different environments. This sequential firing is common to multiple modalities beyond space, suggesting that hippocampal activity can anchor to the most behaviorally relevant or salient aspects of experience. As reward is a highly salient event, we hypothesized that broad sequences of hippocampal activity can likewise become anchored relative to reward. To test this hypothesis, we performed two-photon imaging of calcium activity in hippocampal area CA1 as mice navigated virtual linear environments with multiple changing hidden reward locations. We found that when the reward moved, a subpopulation of cells remapped to the same relative position with respect to reward, including previously undescribed cells with fields distant from reward. These reward-relative cells constructed sequences that spanned the task structure irrespective of spatial stimuli. The density of the reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. In contrast, a largely separate subpopulation of cells maintained a place code relative to the spatial environment. These findings provide insight into how separate hippocampal ensembles may flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.

show abstract

“…Topological data analysis (TDA) [42] has become a widely used tool for studying neural population activity [24,[43][44][45][46][47]. A core technique in TDA is persistent homology (Appendix E.1), which can be used to estimate the topology of the underlying neural manifold.…”

Section: Single and Dual Agent Rnns Learn Different Representations A...mentioning

confidence: 99%

“…E.1 Topological data analysis TDA [42] has become a widely used tool in neuroscience [24,[43][44][45][46][47]. In particular, it has been leveraged to provide evidence for the population activity of grid cells (from the same module) being constrained to a two-dimensional toroidal manifold [24].…”

Section: E Population Representation Detailsmentioning

confidence: 99%

Not so griddy: Internal representations of RNNs path integrating more than one agent

Redman,

Acosta,

Acosta-Mendoza

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self’s position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for therelativeposition of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

show abstract

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Cited by 10 publications

References 129 publications

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Hippocampal sequences span experience relative to rewards

Not so griddy: Internal representations of RNNs path integrating more than one agent

Contact Info

Product

Resources

About