Grid cells correlation structure suggests organized feedforward projections into superficial layers of the medial entorhinal cortex

Abstract

Navigation requires integration of external and internal inputs to form a representation of location. Part of this integration is considered to be carried out by the grid cells network in the medial entorhinal cortex (MEC). However, the structure of this neural network is unknown. To shed light on this structure, we measured noise correlations between 508 pairs of simultaneous previously recorded grid cells. We differentiated between pure grid and conjunctive cells (pure grid in Layers II, III, and VI vs. conjunctive in Layers III and V--only Layer III was bi-modal), and devised a new method to classify cell pairs as belonging/not-belonging to the same module. We found that pairs from the same module show significantly more correlations than pairs from different modules. The correlations between pure grid cells decreased in strength as their relative spatial phase increased. However, correlations were mostly at 0 time-lag, suggesting that the source of correlations was not only synaptic, but rather resulted mostly from common input. Given our measured correlations, the two functional groups of grid cells (pure vs. conjunctive), and the known disorganized recurrent connections within Layer II, we propose the following model: conjunctive cells in deep layers form an attractor network whose activity is governed by velocity-controlled signals. A second manifold in Layer II receives organized feedforward projections from the deep layers, giving rise to pure grid cells. Numerical simulations indicate that organized projections induce such correlations as we measure in superficial layers. Our results provide new evidence for the functional anatomy of the entorhinal circuit-suggesting that strong phase-organized feedforward projections support grid fields in the superficial layers.

Probabilistic Learning by Rodent Grid Cells

“…SIFM also provides support that oscillatory and attractor dynamics may be complementary [19, 49]. Notably, attractor-like SIFM grid cell correlations arise from shared sensory inputs and convergence in learned information rather than direct coupling between grid cells, consistent with recent noise correlation analysis of rodent grid cells suggesting that relatively little spike train correlation is attributable to direct synaptic connections between cell pairs [50]. …”

Theta sequences of grid cell populations can provide a movement-direction signal

“…Second, experimental work identified that head-direction cells, conjunctive grid by direction cells, and speed cells provide local inputs to grid cell networks [8,9], which is consistent with a number of feedforward and/or recurrent neural network architectures that combine inputs of these cell types with local network computations [10,11]. For example, based on the finding that head direction cells and conjunctive direction by grid cells are preferentially found in deep layers and that grid cells without directional preference are found in layer II [8], many models explicitly include connectivity between different cell types within mEC to perform network computations [12]. …”

Layer-Specific Organization of Local Excitatory and Inhibitory Synaptic Connectivity in the Rat Presubiculum

“…Within the presubiculum, layer II has been proposed to be the principal source of grid activity (Preston-Ferrer et al 2016) while grid fields in superficial layers of the MEC have been suggested to require organized feedforward projections (Tocker et al 2015). Considering the unidirectional projections of the superficial presubiculum to the MEC (Kerr et al 2007; Honda et al 2008; Canto et al 2012), it seems more likely that entorhinal grid activity is inherited from the presubiculum rather than vice versa.…”