Introducing double bouquet cells into a modular cortical associative memory model

Abstract

We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale’s principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and 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. We thus show that the resulting change to the microcircuit architecture improves the model’s biological plausibility without otherwise impacting the model’s spiking activity, basic operation, and learning abilities.

An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity

“…Model networks with negative synaptic weights have been shown to be functionally equivalent to those with both excitatory and inhibitory neurons with only positive weights (Parisien et al, 2008). In the context of this particular model microcircuit and learning rule, this was explicitly and conclusively demonstrated by the addition of double bouquet cells (Chrysanthidis et al, 2019).…”

“…In this model, we focus on layers 2/3, as its high degree of recurrent connectivity (Thomson et al, 2002;) supports attractor function. The high fine-scale specificity of dense stellate cell and double-bouquet cell inputs (DeFelipe et al, 2006;Chrysanthidis et al, 2019) enable strongly coding subpopulations in the superior layers of functional columns. This fits with the general observation that layers 2/3 are more input selective than the lower layers (Crochet and Petersen, 2009;Sakata and Harris, 2009) and thus of more immediate concern to our computational model.…”

Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model

“…For simplicity, we assume that Item and Context networks are located at a substantial distance accounting for the reduced internetwork connection probabilities ( Table 3 ). Each network follows a cortical architecture with modular structure compatible with previous spiking implementations of attractor memory networks ( Lansner, 2009 ; Lundqvist et al, 2011 ; Tully et al, 2014 , 2016 ; Fiebig and Lansner, 2017 ; Chrysanthidis et al, 2019 ; Fiebig et al, 2020 ), and is best understood as a subsampled cortical layer 2/3 patch with nested hypercolumns (HCs) and minicolumns (MCs; Fig. 1 A ).…”

“…Notably, double bouquet cells shown in Figure 1 A are not explicitly simulated, but their effect is nonetheless expressed by the BCPNN rule. A recent study based on a similar single-network architecture (i.e., with the same modular organization, microcircuitry, conductance-based AdEx neuron model, cell count per MC and HC) demonstrated that learned mono-synaptic inhibition between competing attractors is functionally equivalent to the disynaptic inhibition mediated by double bouquet and basket cells ( Chrysanthidis et al, 2019 ). Parameters characterizing other neural and synaptic properties including BCPNN can be found in Table 1 .…”

Comparison Between Human and Rodent Neurons for Persistent Activity Performance: A Biologically Plausible Computational Investigation

“…These LBCs and MCs have large clusters of axons that extend not only across the layers but also across multiple columns. The LBCs have electrophysiological properties similar to those of PCs, meaning that their neuronal parameters are the same as those of PCs in the respective layers (Chrysanthidis et al, 2019). Neurons were distributed over the five cell types in each layer based on estimates from the literature (Markram et al, 2004).…”