Purkinje neuron network (Zang et al. 2020)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:266799
Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.
Reference:
1 . Zang Y, Hong S, De Schutter E (2020) Firing rate-dependent phase responses of Purkinje cells support transient oscillations. Elife [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; Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Phase Response Curves; Action Potentials; Spatio-temporal Activity Patterns; Synchronization; Action Potential Initiation; Oscillations;
Implementer(s): Zang, Yunliang ; Hong, Sungho [shhong at oist.jp];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell;
/
PRC_network_code
figure6
mod
abBK.mod *
apthreshold.mod *
CaP_Raman.mod *
cdp_spiny.mod *
cdp10AIS.mod *
cdp20N_FD2.mod *
cdp4N.mod *
distr.mod *
ihnew.mod *
Isinunoisy.mod *
kv11.mod *
Kv1A.mod *
kv3.mod *
Kv34.mod *
kv4hybrid2.mod *
kv4s.mod *
mslo.mod *
nap.mod *
peak.mod *
pkjlk.mod *
rsgold.mod *
SK2.mod *
syn2.mod *
TCa.mod *
job_script *
                            
#!/bin/bash

#SBATCH --job-name=gcon
#SBATCH --mail-user=yunliang.zang@oist.jp
#SBATCH --ntasks=100
#SBATCH --partition=compute
#SBATCH --mem-per-cpu=2g
#SBATCH --time=7-0
module purge
module load intel/2017
module load intel.mpi/2017
module load python/2.7.10
module load openmpi.icc/1.8.6

export CC=/apps/lic/intel/2017/impi/2017.0.098/intel64/bin/mpicc
export CXX=/apps/lic/intel/2017/impi/2017.0.098/intel64/bin/mpicxx
export CORENEURON_BASE_DIR=/apps/unit/DeSchutterU/coreneuron_envirn/intel
export CORENEURON_INSTALL_DIR=$CORENEURON_BASE_DIR/install
export CORENEURON_SRC_DIR=$CORENEURON_BASE_DIR/src
NEURON_HOME=CORENEURON_INSTALL_DIR
export PATH=$CORENEURON_INSTALL_DIR/x86_64/bin:$CORENEURON_INSTALL_DIR/bin:$PATH
LD_LIBRARY_PATH=/apps/lic/intel/2017/impi/2017.0.098/intel64/lib:$LD_LIBRARY_PATH
export MODLUNIT=$CORENEURON_INSTALL_DIR/share/nrnunits.lib
export LD_LIBRARY_PATH

srun --mpi=pmi2 stdbuf -o0 -e0 ./x86_64/special -mpi PC_net_100.hoc