A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial remapping and grid cell field-to-field variability

Burkitt

2020

Preprint

Experimental studies of grid cells in the Medial Entorhinal Cortex (MEC) have shown that they are selective to an array of spatial locations in the environment that form a hexagonal grid. However, place cells in the hippocampus are only selective to a single-location of the environment while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations, but lack spatial periodicity. Given the anatomical connection from MEC to the hippocampus, previous feedforward models of grid-to-place have been proposed. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the navigational system of the brain in the hippocampus.Our results show that the hexagonal patterns of grid cells with various orientations, grid spacings and phases are necessary for model cells to learn a single spatial field that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of cells in the network, this will lead to the emergence of place cells that have multiple firing locations. More surprisingly, the model shows that place cells can also emerge even when non-negative sparse coding is used with weakly-tuned MEC cells, instead of MEC grid cells, as the input to place cells. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.

Section: Discussionmentioning

confidence: 99%

Learning an efficient hippocampal place map from entorhinal inputs using non-negative sparse coding

Burkitt

2020

Preprint

“…Therefore, feedforward connection from the EC to the hippocampus, recurrent connection within the hippocampus, and feedback connection from the hippocampus to the EC all play an important role, though their specific contributions to the overall function of the network have not been fully uncovered yet. Rennó-Costa and Tort (2017) and Agmon and Burak (2020) investigated the coupling relationship between MEC grid cells and hippocampal place cells and showed that the proposed models can account for some experimental observations. However, how the underlying neural circuits can be implemented is still unclear.…”

Section: Underlying Neural Circuitsmentioning

confidence: 99%

Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding

Burkitt

2021

eNeuro

“…Early computational models of place cells were mostly based upon a feedforward structure, where cells in the EC provide spatial input to the hippocampus (Solstad et al, 2006;Franzius et al, 2007b,a;de Almeida et al, 2009). Some more recent studies have adopted a loop network structure in which cells in the EC project to the hippocampus and also receive feedback from it (Rennó-Costa and Tort, 2017;Li et al, 2020;Agmon and Burak, 2020). Models incorporating this loop network structure can explain more experiment observations, especially on how hippocampal place cells affect the firing of MEC grid cells.…”

Section: The Entorhinal-hippocampal Loopmentioning

confidence: 99%

Learning spatio-temporal properties of hippocampal place cells

2021

Preprint

Hippocampal place cells have spatio-temporal properties: they generally respond to a single spatial position of a small environment; in addition, they also display a temporal property, the theta phase precession, namely that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in layer II of medial entorhinal cortex (MEC) also have spatio-temporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Other non-grid spatial cells are also abundant in the entorhinal cortex (EC). EC is the upstream that projects strongly to the hippocampus, many EC-hippocampus models have been designed to explain how the spatial property of place cells emerges. However, there is no learning model explaining how the temporal properties of hippocampal place cells emerge from the EC input. A learning model is presented here based on non-negative sparse coding in which we show that the spatial and temporal properties of hippocampal place cells can be simultaneously learnt from EC input: both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells while MEC grid cells contribute to the temporal properties of hippocampal place cells.