Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/164298
Title: Modulating grid cell scale and intrinsic frequencies via slow high-threshold conductances: A simplified model
Author: Santos-Pata, Diogo
Zucca, Riccardo
López-Carral, Héctor
Verschure, Paul
Keywords: Escorça cerebral
Neurones
Orientació animal
Cerebral cortex
Neurons
Animal orientation
Issue Date: 29-Jul-2019
Publisher: Elsevier
Abstract: Grid cells in the medial entorhinal cortex (MEC) have known spatial periodic firing fields which provide a metric for the representation of self-location and path planning. The hexagonal tessellation pattern of grid cells scales up progressively along the MEC’s layer II dorsal-to-ventral axis. This scaling gradient has been hypothesized to originate either from inter-population synaptic dynamics as postulated by attractor networks, or from projected theta frequency waves to different axis levels, as in oscillatory models. Alternatively, cellular dynamics and specifically slow high-threshold conductances have been proposed to have an impact on the grid cell scale. To test the hypothesis that intrinsic hyperpolarization-activated cation currents account for both the scaled gradient and the oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. We observed that the intrinsic neuronal membrane properties of simulated cells were sufficient to induce an increase in grid scale and potentiate differences in the membrane potential oscillatory frequency. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells’ scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.neunet.2019.06.011
It is part of: Neural Networks, 2019, vol. 119, p. 66-73
URI: http://hdl.handle.net/2445/164298
Related resource: https://doi.org/10.1016/j.neunet.2019.06.011
Appears in Collections:Articles publicats en revistes (Institut de Bioenginyeria de Catalunya (IBEC))



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