Phase response curves firing rate dependency of rat purkinje neurons in vitro (Couto et al 2015)

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NEURON implementation of stochastic gating in the Khaliq-Raman Purkinje cell model. NEURON implementation of the De Schutter and Bower model of a Purkinje Cell. Matlab scripts to compute the Phase Response Curve (PRC). LCG configuration files to experimentally determine the PRC. Integrate and Fire models (leaky and non-leaky) implemented in BRIAN to see the influence of the PRC in a network of unconnected neurons receiving sparse common input.
1 . Couto J, Linaro D, De Schutter E, Giugliano M (2015) On the Firing Rate Dependency Of the Phase Response Curves of rat Purkinje Neurons In Vitro PLOS Comp Biol [PubMed]
2 . Linaro D, Couto J, Giugliano M (2014) Command-line cellular electrophysiology for conventional and real-time closed-loop experiments. J Neurosci Methods 230:5-19 [PubMed]
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): Cerebellum purkinje cell;
Gap Junctions:
Simulation Environment: NEURON; MATLAB; Brian; LCG; Python;
Model Concept(s): Phase Response Curves;
Implementer(s): Couto, Joao [jpcouto at];
Search NeuronDB for information about:  Cerebellum purkinje cell;
This directory contains files to reproduce the simulations presented
in the paper:

Couto, J., Linaro, D., De Schutter, E. and Giugliano, M.  "On the
Firing Rate Dependency Of the Phase Response Curves of rat Purkinje
Neurons In Vitro". PLOS Comp Biol 2015

The subdirectories contain the following items.

1. mod-files - contains the mod files that can be used in NEURON to
simulate the Khaliq-Raman Purkinje cell model using stochastic
representations of the ion channels (as well as the original mod
files) and the mod files for the NEURON version of the De Schutter and
Bower Purkinje Cell model.

2 python - contains the python scripts to perform the simulations in
Fig. 6 (KR folder) and Figs S1, S3 and S4 (DSB folder).

3. matlab - contains the minimal scripts to compute the PRC using the
direct method. Both the traditional and corrected (Phoka et al. 2010)
methods are available.

4. lcg - contains the configuration files that can be used to
reproduce the experiments with frequency clamp. Read the README.txt
file in this directory.

5. brian - contains BRIAN script to perform network simulations of
non-leaky LIF neurons that approximate the flat PRC profile described
for low firing rates.

For any question, please contact: 
	Joao Couto -
	Daniele Linaro -
	Erik De Schutter - 
	Michele Giugliano -

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