In vivo imaging of dentate gyrus mossy cells in behaving mice (Danielson et al 2017)

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Accession:206397
Mossy cells in the hilus of the dentate gyrus constitute a major excitatory principal cell type in the mammalian hippocampus, however, it remains unknown how these cells behave in vivo. Here, we have used two-photon Ca2+ imaging to monitor the activity of mossy cells in awake, behaving mice. We find that mossy cells are significantly more active than dentate granule cells in vivo, exhibit significant spatial tuning during head-fixed spatial navigation, and undergo robust remapping of their spatial representations in response to contextual manipulation. Our results provide the first characterization of mossy cells in the behaving animal and demonstrate their active participation in spatial coding and contextual representation.
Reference:
1 . Danielson NB, Turi GF, Ladow M, Chavlis S, Petrantonakis PC, Poirazi P, Losonczy A (2017) In Vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice. Neuron 93:552-559.e4 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s): Dentate gyrus granule GLU cell; Dentate gyrus basket cell; Dentate gyrus hilar cell; Dentate gyrus mossy cell; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: Brian; Python;
Model Concept(s): Pattern Separation;
Implementer(s): Chavlis, Spyridon [schavlis at imbb.forth.gr]; Petrantonakis, Panagiotis C. ; Poirazi, Panayiota [poirazi at imbb.forth.gr];
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; NMDA; 
Author: Spyridon Chavlis, schavlis [at] imbb.forth.gr

Code for "In vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice", Danielson et al., 2017

This code replicates the results Figure 4 of the aforementioned paper.

1. connectivityMatrices.py constructs the network and saves the connectvities in ConnectivityMatrices folder. scale_frac represents the scale of the network

from console run: python connectivityMatrices.py

2. input_patterns.py creates the inputs to the network. You should run at least 50 times (50 different trials) in order to replicate the results

from console run: for i in $(seq 1 50);do python input_patterns.py $i;done


Experiment: In line with Chavlis et al., 2017 we run an INITIAL pattern and an overlapping one 80% overlap.

a. DG_INITIAL.py and DG_80.py --> Control
b. DG_INITIAL_noMC.py and DG_80_noMC.py --> Mossy Cells full deletion
c. DG_INITIAL_noMCBC.py and DG_80_noMCBC.py --> Mossy Cell to Basket Cells deletion
d. DG_INITIAL_noMCGC.py and DG_80_noMCGC.py --> Mossy Cell to Granule Cell deletion

As the model was run under an HPC cluster you should run the experiments at least 50 times. Parameter to change from run to run is trial_i = [1],trial_i = [2] etc.


The results are stored under results folder in an approipriate location that the code creates.


Analysis of the results

python Analysis_figure4.py