Entorhinal velocity signals reflect environmental geometry

Munn, Robert G.K.; Mallory, Caitlin S.; Hardcastle, Kiah; Chetkovich, Dane M.; Giocomo, Lisa M.

doi:10.1101/671222

Cited by 13 publications

(12 citation statements)

References 57 publications

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“…Two candidates for such a speed signal terminating in the MEC have been proposed: a speed signal by firing rate, most clearly present in a subpopulation of "speed cells" showing positive and linear speed tuning curves (Kropff et al, 2015); and an oscillatory speed signal by changes in LFP theta frequency or by changes in the theta rhythmic firing frequency of individual MEC neurons (Burgess, 2008;Hinman et al, 2016). Both speed signals can change in response to changes of the size and shape of the environment (Munn et al, 2020), as do grid cells (C. Barry et al, 2007;Krupic et al, 2015). In this study, we examined how changes in spatial stability of grid cells relate to Notably, the observed saturating exponential component in changes of grid cell spatially periodic firing supports an earlier report by Pérez-Escobar et al (2016).…”

Section: Resultsmentioning

confidence: 99%

Effects of visual inputs on neural dynamics for coding of location and running speed in medial entorhinal cortex

Dannenberg

Lazaro

Nambiar

et al. 2020

Preprint

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11Neuronal representations of spatial location and movement speed are important for a broad 12 range of cognitive functions, including spatial self-localization and memory-guided navigation. 13Two possible speed signals, by theta frequency or by firing rate, have been hypothesized to 14 provide the velocity signal needed for generating the spatially periodic grid cell firing pattern. 15However, which of these speed signals is utilized by the brain remains unknown. By 16 manipulating visual inputs and analyzing the time-courses of evoked changes, we demonstrate 17 that changes in spatial stability of grid cell firing correlate in time with changes in speed coding 18 by local field potential theta frequency. In contrast, visual inputs do not affect speed coding by 19 firing rate even if baseline firing rates are changed. Moreover, grid cells maintain a spatially 20 periodic firing pattern, though less stable, in complete darkness. These data suggest that mice 21 use an oscillatory speed signal to perform path integration. 22 23 ( Supplementary Fig. 1B & C). 126 127Local field potential activity in the MEC primarily reflects the summed activity of synaptic inputs 128 to the MEC, which is likely to be correlated with firing patterns of MEC neurons. Many neurons 129 in the MEC show a theta rhythmic firing pattern and-similarly to the LFP theta frequency-the 130 frequency of theta rhythmic firing increases with running speed (Hinman et al., 2016). In our 131 data set, we identified a total of n = 342 neurons from 14 mice as theta modulated based on 132 their autocorrelograms. We asked if and how theta rhythmic firing in these theta modulated 133 MEC neurons is affected by removing and reinstating all visual inputs. Towards that aim we 134 used an MLE approach (Climer et al., 2015) to fit a model of theta spiking rhythmicity to the 135 observed spike train autocorrelations and identify the frequency and magnitude of theta 136

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Section: Resultsmentioning

confidence: 99%

Effects of visual inputs on neural dynamics for coding of location and running speed in medial entorhinal cortex

Dannenberg

Lazaro

Nambiar

et al. 2020

Preprint

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“…Grid cells are thought to provide a universal, that is, environment-invariant, metric for spatial navigation based on the use of idiothetic cues (McNaughton et al, 2006). It has since been shown that the grid map is not as environmentindependent as previously assumed (Barry et al, 2007(Barry et al, , 2012Boccara et al, 2019;Derdikman et al, 2009;Jacob et al, 2019;Krupic et al, 2015;Munn et al, 2020). Yet, the MEC-grid cellspath integration hypothesis remains predominant despite the fact that only a few studies have shown a link between grid cell activity and path integration (Allen et al, 2014;Gil et al, 2018;Jacob et al, 2019) and between the MEC and path integration (Jacob et al, 2017;Van Cauter et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Medial entorhinal cortex lesions induce degradation of CA1 place cell firing stability when self-motion information is used

Philippe

Cauter

Poucet

et al. 2020

Brain and Neuroscience Advances

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The entorhinal–hippocampus network plays a central role in navigation and episodic memory formation. To investigate these interactions, we examined the effect of medial entorhinal cortex lesions on hippocampal place cell activity. Since the medial entorhinal cortex is suggested to play a role in the processing of self-motion information, we hypothesised that such processing would be necessary for maintaining stable place fields in the absence of environmental cues. Place cells were recorded as medial entorhinal cortex–lesioned rats explored a circular arena during five 16-min sessions comprising a baseline session with all sensory inputs available followed by four sessions during which environmental (i.e. visual, olfactory, tactile) cues were progressively reduced to the point that animals could rely exclusively on self-motion cues to maintain stable place fields. We found that place field stability and a number of place cell firing properties were affected by medial entorhinal cortex lesions in the baseline session. When rats were forced to rely exclusively on self-motion cues, within-session place field stability was dramatically decreased in medial entorhinal cortex rats relative to SHAM rats. These results support a major role of the medial entorhinal cortex in processing self-motion cues, with this information being conveyed to the hippocampus to help anchor and maintain a stable spatial representation during movement.

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“…Our framework also provides algorithmic-and implementation-level explanations for observed features of grid cell firing in response to manipulations 7,9,[21][22][23][24] or inhomogeneity [25][26][27][28][29] of environmental sensory input. Overall, these phenomena can be understood in a probabilistic framework, where minimization of prediction errors between the transition and observation models are traded against prior model beliefs.…”

Section: Introductionmentioning

confidence: 99%

Replay as structural inference in the hippocampal-entorhinal system

Evans¹,

Burgess²

2020

Preprint

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Model-based decision making relies on the construction of an accurate representation of the underlying state-space, and localization of one’s current state within it. One way to localize is to recognize the state with which incoming sensory observations have been previously associated. Another is to update a previous state estimate given a known transition. In practice, both strategies are subject to uncertainty and must be balanced with respect to their relative confidences; robust learning requires aligning the predictions of both models over historic observations. Here, we propose a dual-systems account of the hippocampal-entorhinal system, where sensory prediction errors between these models during online exploration of state space initiate offline probabilistic inference. Offline inference computes a metric embedding on grid cells of an associative place graph encoded in the recurrent connections between place cells, achieved by message passing between cells representing non-local states. We provide testable explanations for coordinated place and grid cell ‘replay’ as efficient message passing, and for distortions, partial rescaling and direction-dependent offsets in grid patterns as the confidence weighted balancing of model priors, and distortions to grid patterns as reflecting inhomogeneous sensory inputs across states.Author SummaryMinimising prediction errors between transition and sensory input (observation) models predicts partial rescaling and direction-dependent offsets in grid cell firing patterns.Inhomogeneous sensory inputs predict distortions of grid firing patterns during online localisation, and local changes of grid scale during offline inference.Principled information propagation during offline inference predicts coordinated place and grid cell ‘replay’, where sequences propagate between structurally related features.

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Entorhinal velocity signals reflect environmental geometry

Cited by 13 publications

References 57 publications

Effects of visual inputs on neural dynamics for coding of location and running speed in medial entorhinal cortex

Effects of visual inputs on neural dynamics for coding of location and running speed in medial entorhinal cortex

Medial entorhinal cortex lesions induce degradation of CA1 place cell firing stability when self-motion information is used

Replay as structural inference in the hippocampal-entorhinal system

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