Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018)

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Accession:244700
" ... Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons."
Reference:
1 . Górski T, Veltz R, Galtier M, Fragnaud H, Goldman JS, Telenczuk B, Destexhe A (2018) Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. J Comput Neurosci 45:223-234 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire neuron; Hodgkin-Huxley neuron;
Channel(s): I Sodium; I Potassium;
Gap Junctions:
Receptor(s): Gaba; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2;
Model Concept(s): Influence of Dendritic Geometry; Synaptic Integration;
Implementer(s): Górski, Tomasz [gorski at inaf.cnrs-gif.fr];
Search NeuronDB for information about:  AMPA; Gaba; I Sodium; I Potassium;
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gorski2018
readme.txt
corr_AE.py
corr_HH.py
corr_measure.py
inputs.py
                            



This model aims to measure somatic firing rate
responses to correlated synaptic activity for Hodgkin-Huxley
(HH) or integrate-and-fire (IF) point neuron and for multi-compartment HH or IF neuron with passive or active dendrite.


This model is written in Python 3 language using Brian2 neuronal simulator.



How to run it?
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To run it one has to install Brian2 simulator
https://brian2.readthedocs.io/en/stable/

To run simulation of HH neuron
run corr_HH.py


To run simulation of IF neuron
run corr_IF.py


All parameters like morphology, number of synapses etc. are set in corr_HH.py and corr_IF.py.
One can scan over different paramenters (input rates, synaptic conductances, dendritic channel densities etc.). 
Results are somatic firing responses for increasing correlation of synaptic input.  


Correlated synaptic input is generated by
inputs.py.


Description of the model and results from model
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The paper in which this model was used can be found
here:
https://www.biorxiv.org/content/early/2017/05/14/137984