Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)

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Accession:206372
Here we investigate the role of dentate granule cell dendrites in pattern separation. The model consists of point neurons (Integrate and fire) and in principal neurons, the granule cells, we have incorporated various number of dendrites.
Reference:
1 . Chavlis S, Petrantonakis PC, Poirazi P (2017) Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity. Hippocampus 27:89-110 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Realistic Network;
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s): Dentate gyrus granule cell; Dentate gyrus basket cell; Dentate gyrus mossy cell; Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Abstract integrate-and-fire leaky neuron; Dentate gyrus HIPP cell;
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 cell; GabaA; AMPA; NMDA;
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ChavlisEtAl2017
12dendrites
3dendrites
6dendrites
Neurons
readme.txt
                            
This is the readme for the model associated with the paper:

Chavlis S, Petrantonakis PC, Poirazi P (2017) Dendrites of dentate
gyrus granule cells contribute to pattern separation by controlling
sparsity. Hippocampus

This python model was contributed by S Chavlis.

Neurons folder

The codes there are for various validation tests in order to create figure2 and several supplement figures

3,6 and 12 dendrites folders

The main code for every model with 12, 6 and 3 dendrites on Granule Cells.

The code represents one Trial for a given input pattern as well as a specific connectivity in the ConnectivityMatrices_#dendrites folder

Need 50 Trials of each code with 50 different input patterns. 

Each main code should run for different overlaps, specifric comments inside code.


For many input patterns use the following code inputs.py

#####################################################
from brian import  reinit,clear
import numpy as np
import random as pyrandom
import sys

def input_patterns(trial_i):
    reinit(states = True)
    clear(erase = True, all = True)

    Trial = trial_i[0]
    # Initial pattern
    scale_fac = 2

    N_input   = 100 * scale_fac
    d_input   = 0.10 # active input density

    # Active pattern of neurons
    active   = sorted(pyrandom.sample(xrange(N_input), int(d_input*N_input)))
    np.save('active_pattern_'+str(Trial)+'.npy', active)

    return

jobidx = int(sys.argv[1])
results = input_patterns([jobidx]) # launches multiple processes
#####################################################


python inputs.py <number form 1 to 50>