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.
Model Type: Realistic Network
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
Model Concept(s): Learning
Simulation Environment: NEST