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 curve of rat Purkinje neurons in vitro. PLoS Comput Biol 11:e1004112 [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]
Citations  Citation Browser
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 GABA 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 GABA cell;
The mod-files contained in this directory implement the Khaliq-Raman
model, described in the papers:

1. Khaliq ZM, Gouwens NW, Raman IM (2003) The contribution of
resurgent sodium current to high-frequency firing in Purkinje neurons:
an experimental and modeling study. J Neurosci 23:4899-912.
2. Raman IM, Bean BP (2001) Inactivation and recovery of sodium
currents in cerebellar Purkinje neurons: evidence for two
mechanisms. Biophys J 80:729-37.

The following mod-files are the original ones (see entry 48332 on
ModelDB) and they implement the deterministic equations of the model.


The following mod-files are the modified versions of the
correspondingly named ones and implement channel noise according to
the algorithm described in

Linaro, D., Storace, M., & Giugliano, M. (2011). Accurate and fast
simulation of channel noise in conductance-based model neurons by
diffusion approximation. PLoS Computational Biology, 7(3),
e1001102. doi:10.1371/journal.pcbi.1001102

available on ModelDB via accession number 127992.


One of the mod-files (rsg_cn.mod) implementing channel noise in the Khaliq-Raman model
requires the usage of the GNU scientific library. This also requires recompiling NEURON
so that nrnivmodl links the GSL when compiling the mod-files.

In order to achieve this, first install the GSL on your system, either manually or
using one of the many package managers available for this purpose. Then, go to the
directory where you have installed NEURON: here, I will assume that you have followed
the instructions for compiling NEURON from source on a Linux machine, which are available
at the address

$ cd $HOME/neuron/nrn

Set the environment variable $LDFLAGS to point to the additional libraries you want
nrnivmodl to link, namely libgsl and libgslcblas. To do so, enter at a prompt

$ export LDFLAGS='-lgsl -lgslcblas'

This assumes that the library files are in a directory where the linker can find them.
If that's not the case, you should add the path of that directory (here we will assume
it is /usr/local/lib) to $LDFLAGS:

$ export LDFLAGS='-L'$HOME'/local/lib -lgsl -lgslcblas'

Additionally, it is necessary to set the variable $CFLAGS to point to the directory
where the header files for the libraries are stored (in this example, /usr/local/include).
This step is necessary only if they are in a folder not in the compiler path.

$ export CFLAGS='-I'$HOME'/local/include'

You can now reconfigure and recompile NEURON by issuing the commands

$ make clean
$ ./configure --prefix=`pwd`
$ make
$ make install

This will cause nrnivmodl to _always_ link the gsl libraries whenever it compiles
a MOD-file.