Recurrent amplification of grid‐cell activity

D'Albis, Tiziano; Kempter, Richard

doi:10.1002/hipo.23254

Cited by 7 publications

(5 citation statements)

References 95 publications

(190 reference statements)

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“…Such grid-tuned feedforward input to the MEC-hippocampal loop via L2P grid cells could supervise the development of recurrent connectivity underlying CAN dynamics (76,77). In this way, spatially-tuned activity of L2Ps would serve also to anchor the grids produced by CAN networks to physical space (9,65,198,(294)(295)(296).…”

Section: L2s -> L2p or L2s -> L3pmentioning

confidence: 99%

“…One explanation could be that grid firing in L2P cells is generated by mechanisms that are fundamentally different from those underlying CAN models: during a developmental phase, grid firing in L2Ps could be guided by mechanisms that combine spatially tuned input (for example from parasubiculum), synaptic plasticity, and cell-intrinsic dynamics ( 73 – 80 ). Such grid-tuned feedforward input to the MEC-hippocampal loop via L2P grid cells could supervise the development of recurrent connectivity underlying CAN dynamics ( 318 , 319 ). In this way, spatially tuned activity of L2Ps would serve also to anchor the grids produced by CAN networks to physical space ( 48 , 220 , 320 – 323 ).…”

Section: Microcircuitsmentioning

confidence: 99%

“…This enables the animal to have different sets of active neurons for different environments. Location-correlated input can also serve to periodically correct the abovementioned drift and may serve to “teach” the network during development, such that the connectivity underlying CAN dynamics becomes linked to spatial coding properties ( 71 , 318 , 319 ).…”

Section: Appendix: Continuous Attractor Networkmentioning

confidence: 99%

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Microcircuits for spatial coding in the medial entorhinal cortex

Tukker

Beed

Brecht

et al. 2022

Physiological Reviews

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The hippocampal formation is critically involved in learning and memory, and contains a large proportion of neurons encoding aspects of the organism's spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields, as well as a host of other functionally defined cell types including head-direction cells, speed cells, border cells, and object vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid-pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.

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Section: L2s -> L2p or L2s -> L3pmentioning

confidence: 99%

Section: Microcircuitsmentioning

confidence: 99%

Section: Appendix: Continuous Attractor Networkmentioning

confidence: 99%

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Microcircuits for spatial coding in the medial entorhinal cortex

Tukker

Beed

Brecht

et al. 2022

Physiological Reviews

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“…Local recurrent connections have been identified as a key element of grid pattern formation [45], in particular to align the grids in a flat environment [35,46,47]. We asked how the strength of recurrent connections affects the pattern formation process in our model environments.…”

Section: Grid Maps In Burrows and Their Stability (Top And Center) The Maps Of One Unit In Chambers (The Lower Half Of Each Chamber Is Shmentioning

confidence: 99%

Grid Cells Lose Coherence in Realistic Environments

Luo¹,

Toso²,

Si³

et al. 2022

Hippocampus - Cytoarchitecture and Diseases

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Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an incoherent motley of unit clusters, flexible or outright unstable.

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“…The functional organization between these two brain regions was long studied, discussing whether the hippocampus or MEC drives the activity of the other region [33][34][35][36][37] . Several works arose on how grid cells and place cells could affect each other [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] . Recently, theoretical studies have started characterizing the dynamic relationship between the hippocampus and MEC with a network of loops 24,53 and a reciprocal connectivity that enables the error reduction mechanism for path integration 54 .…”

mentioning

confidence: 99%

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

Fernández-León

Uysal

2022

Sci Rep

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Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.

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Recurrent amplification of grid‐cell activity

Cited by 7 publications

References 95 publications

Microcircuits for spatial coding in the medial entorhinal cortex

Microcircuits for spatial coding in the medial entorhinal cortex

Grid Cells Lose Coherence in Realistic Environments

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

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