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

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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.
1 . Chrysanthidis N, Fiebig F, Lansner A (2019) Introducing double bouquet cells into a modular cortical associative memory model Journal of Computational Neuroscience
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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;
Gap Junctions:
Simulation Environment: NEST;
Model Concept(s): Learning;
Implementer(s): Chrysanthidis, Nikolaos [nchr at]; Fiebig, Florian [fiebig at]; Lansner, Anders [ala at];
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 interneuron basket PV GABA cell;
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/* lt__alloc.c -- internal memory management interface

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#include "lt__alloc.h"

static void alloc_die_default (void);

void (*lt__alloc_die) (void) = alloc_die_default;

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static void
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