Models that contain the Cell : Entorhinal cortex stellate cell

(The layer II entorhinal stellate neurons are the projection neurons that are the main source of the perforant path into the hippocampus.)
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    Models   Description
1.  Active dendritic integration in robust and precise grid cell firing (Schmidt-Hieber et al 2017)
"... Whether active dendrites contribute to the generation of the dual temporal and rate codes characteristic of grid cell output is unknown. We show that dendrites of medial entorhinal cortex neurons are highly excitable and exhibit a supralinear input–output function in vitro, while in vivo recordings reveal membrane potential signatures consistent with recruitment of active dendritic conductances. By incorporating these nonlinear dynamics into grid cell models, we show that they can sharpen the precision of the temporal code and enhance the robustness of the rate code, thereby supporting a stable, accurate representation of space under varying environmental conditions. Our results suggest that active dendrites may therefore constitute a key cellular mechanism for ensuring reliable spatial navigation."
2.  An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)
A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.
3.  Estimation of conductance in a conductance-based model of quadratic type (Vich & Guillamon 2015)
We assume to have a quadratic approximation of a conductance-based neuron model, as in H.Rotstein (2015). Given the resulting membrane potential (v) and the course of the gating variable (w), this program estimates the synaptic current that the neuron is receiving at each time.    Moreover, given the voltage traces for two different applied (steady) currents and the excitatory and inhibitory reversal potentials, the program estimates the excitatory and inhibitory conductances separately.   Finally, the program gives the option of estimating the synaptic conductance. This conductance can be estimated in two different ways: (1) if only one voltage trace is given, the synaptic conductance is estimated using the synaptic reversal potential; (2) however, if two voltage traces are given (for two different applied currents), then the synaptic conductance can be either estimated using the synaptic reversal potential or the leak conductance.
4.  Grid cell spatial firing models (Zilli 2012)
This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells.
5.  MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)
This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.
6.  Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)
"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative location during remapping. This process would appear to require some form of modulated global input. Grid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source. However, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007). The hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently unknown. We study whether dividing grid cells into small numbers of independently realigning modules can both account for this localized coherence and allow for hippocampal remapping. ..."
7.  ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons (Bingham et al 2020)
"... a Ruled-Optimum Ordered Tree System (ROOTS) was developed that extends the capability of neuronal morphology generative methods to include highly branched cortical axon terminal arbors. Further, this study presents and explores a clear use-case for such models in the prediction of cortical tissue response to externally applied electric fields. The results presented herein comprise (i) a quantitative and qualitative analysis of the generative algorithm proposed, (ii) a comparison of generated fibers with those observed in histological studies, (iii) a study of the requisite spatial and morphological complexity of axonal arbors for accurate prediction of neuronal response to extracellular electrical stimulation, and (iv) an extracellular electrical stimulation strength–duration analysis to explore probable thresholds of excitation of the dentate perforant path under controlled conditions. ..."
8.  Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces
9.  Synaptic integration by MEC neurons (Justus et al. 2017)
Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes.
10.  Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)
A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.

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