I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)

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Accession:152539
Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)
Reference:
1 . Cavallari S, Panzeri S, Mazzoni A (2014) Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks Frontiers in Neural Circuits 8:12
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns;
Implementer(s): Cavallari, Stefano [stefano.cavallari at iit.it];
Search NeuronDB for information about:  GabaA; AMPA;
  
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CavallariEtAl2014__7_2015
LIF_COBN
LIF_CUBN
readme.txt
                            
This is the readme for the two neural network models (mex file) associated with the following paper:

S.Cavallari, S. Panzeri and A.Mazzoni (2014) Comparison of the
dynamics of neural interactions between current-based and conductance-based 
integrate-and-fire recurrent networks, Frontiers in Neural Circuits
8:12. doi: 10.3389/fncir.2014.00012

Recurrent networks, each of two populations (excitatory and inhibitory) of
randomly connected Leaky Integrate-and-Fire (LIF) neurons with either
conductance-based synapses (COBN) or current-based synapses (CUBN)
were studied. The activity of the LIF COBN model were compared with the
activity of the associated model LIF CUBN. 
Instructions are provided in the ReadMe files in each model associated sub folder, LIF_COBN and
LIF_CUBN.

If you have any questions about the implementation of these matlab
models, which require compilation with mex, contact:
ste.cavallari@gmail.com

Please cite the paper if you use the codes.


20140709 Comments in LIF_COBN/code_COBN.c, LIF_COBN/code_COBN.m,
LIF_CUBN/code_CUBN.c, LIF_CUBN/code_CUBN.m were enhanced.

20150722 The code to generate the OU process has been added (LIF_COBN/OU_process.m
and LIF_CUBN/OU_process.m) together with the instructions
(LIF_COBN/ReadMe_COBN and LIF_CUBN/ReadMe_CUBN)
to set the arguments to generate the data used in some figures of the paper.

Cavallari S, Panzeri S, Mazzoni A (2014) Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks Frontiers in Neural Circuits 8:12

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