An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)

 Download zip file 
Help downloading and running models
Accession:257610
We present an electrophysiological model of double bouquet cells (DBCs) and integrate them into an established cortical columnar microcircuit model that implements a BCPNN (Bayesian Confidence Propagation Neural Network) learning rule. The proposed architecture effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. The introduction of DBCs improves the biological plausibility of our model, without affecting the model's spiking activity, basic operation, and learning abilities.
Reference:
1 . Chrysanthidis N, Fiebig F, Lansner A (2019) Introducing double bouquet cells into a modular cortical associative memory model Journal of Computational Neuroscience
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 interneuron basket PV GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Neocortex layer 2-3 interneuron; Neocortex bitufted interneuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEST;
Model Concept(s): Learning;
Implementer(s): Chrysanthidis, Nikolaos [nchr at kth.se]; Fiebig, Florian [fiebig at kth.se]; Lansner, Anders [ala at kth.se];
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 interneuron basket PV GABA cell;
Simulation code accompanying the manuscript:
"Introducing double bouquet cells into a modular cortical associative memory model"
By Nikolaos Chrysanthidis, Florian Fiebig, Anders Lansner
Manuscript submitted to Springer, Journal of Computational Neuroscience (JCNS)


We use NEST (Neural Simulation Tool) version 2.4.2 along with Python 2.7
To install NEST 2.4.2 please follow the instructions at the following link: NEST 2.4.2 https://nest-simulator.readthedocs.io/en/latest/installation/oldvers_install.html

In particular and as far as the simulation code is concerned, 
In the DBCmodel.py, the parameters we used to achieve satisfactory electrophysiological fidelity are included.
The simulations aim at reproducing spike patterns under sweeps of increasing  suprathreshold current 
steps (10 pA each) and other reported activity. The range of the stimulation input current is on the same level with the one reported in the paper below.
    
The spike patterns produced (figure DBC_ActivityPatterns) can be directly compared with the findings of fig.4B appeared in Cluster 
analysis–Based Physiological Classification and Morphological Properties of Inhibitory Neurons in Layers 2–3 of Monkey Dorsolateral Prefrontal Cortex (Krimer et al., 2005).

After installing BCPNN module (see. BCPNN_NEST_Module), in order for the figures related to the publication (Introducing double bouquet cells into a modular cortical associative
memory model) to be produced, the following scripts should be compiled: 

mainNewModel.py, plots.py in Figure 1C+3A folder --> Fig.1C (Membrane voltage of a stimulated DBC), Fig.3A (Spike raster of neurons in HC0). For plotting purposes (Fig.1C) the resolution is set to 0.001 for high spikes' amplitude quality.

multipleRoundsNewModel.py, multipleRoundsPreviousModel.py, WeightsDistribution.py in Figure 2 folder --> Fig.2A, Fig.2B, Fig.2C, Fig.2D (Weights distribution, multi-trial averages). 

mainNewModel.py, mainPreviousModel.py, I_GABA.py in Figure 3B folder --> Fig.3B (Functionality verification - Population averaged total inhibitory input current received by pyramidal cells in MC0 in both architectures)


Loading data, please wait...