Information transmission in cerebellar granule cell models (Rossert et al. 2014)

 Download zip file 
Help downloading and running models
Accession:156733
" ... In this modeling study we analyse how electrophysiological granule cell properties and spike sampling influence information coded by firing rate modulation, assuming no signal-related, i.e., uncorrelated inhibitory feedback (open-loop mode). A detailed one-compartment granule cell model was excited in simulation by either direct current or mossy-fiber synaptic inputs. Vestibular signals were represented as tonic inputs to the flocculus modulated at frequencies up to 20 Hz (approximate upper frequency limit of vestibular-ocular reflex, VOR). Model outputs were assessed using estimates of both the transfer function, and the fidelity of input-signal reconstruction measured as variance-accounted-for. The detailed granule cell model with realistic mossy-fiber synaptic inputs could transmit infoarmation faithfully and linearly in the frequency range of the vestibular-ocular reflex. ... "
Reference:
1 . Rössert C, Solinas S, D'Angelo E, Dean P, Porrill J (2014) Model cerebellar granule cells can faithfully transmit modulated firing rate signals. Front Cell Neurosci 8:304 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum interneuron granule GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Markov-type model;
Implementer(s): Solinas, Sergio [solinas at unipv.it]; Roessert, Christian [christian.a at roessert.de];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell; 
This contains the models and functions as used in:

Rossert C, Solinas S, D'Angelo E, Dean P and Porrill J (2014) Model
cerebellar granule cells can faithfully transmit modulated firing rate
signals. Front. Cell. Neurosci. 8:304. doi: 10.3389/fncel.2014.00304

The Model of the Granule cell used is from: Solinas S., Nieus T,
d'Angelo E. (2010) A Realistic Large-Scale Model of the Cerebellum
Granular Layer Predicts Circuit Spatio-Temporal Filtering
Properties. Front Cell Neurosci. 2010;4:12.

(This code is a snapshot from
https://github.com/croessert/AnalyseGranCellRoessertEtAl14 Version
4e8ce79. Here, also the resulting simulation results can be found.)


1. run ./nrncompule to compile .mod files

2. To run the simulations and plot the figures execute the commands
below.

3. Figures will be saved to: figs/Pub


# FIGURE 1
python Plots_Openloop_Paper_Methods.py -o fig1

# FIGURE 2:
python Plots_Openloop_Paper_Methods.py -o fig2

# FIGURE 3:
python Plots_Openloop_Paper_Results.py -o fig3

# FIGURE 4:
python Plots_Openloop_Paper_Results.py -o fig4

# FIGURE 5:
python Plots_Openloop_Paper_Results.py -o fig4b

# FIGURE 6:
python Plots_Openloop_Paper_Results_syn.py -o fig5

# FIGURE 7:
python Plots_Openloop_Paper_Results_syn.py -o fig6

# FIGURE 8:
python Plots_Openloop_Paper_Results_syn.py -o fig7

# FIGURE 9:
python Plots_Openloop_Paper_Results_syn.py -o fig8b

# FIGURE 10:
python Plots_Openloop_Paper_Results_syn.py -o fig8a


Notes:
- When running simulations with MPI the number of nodes has to be <=
  to number of cells, otherwise error is returned.

All analysis scripts were implemented by Christian Rossert
(christian.a [4t] roessert.de)

Loading data, please wait...