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.

中文翻译：

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将双花束细胞引入模块化的皮层联想记忆模型中。

我们提出了双束细胞的电生理模型，并将其整合到已建立的皮质柱状微电路模型中，该模型先前已用作记忆的峰值吸引子模型。该模型中的学习依赖于Hebbian-Bayesian学习规则来调节锥体细胞之间的经常性连接。我们在这里证明，在生物物理学上似乎可行的双束细胞模型的包含可以解决关于学习规则的早期关注，而学习规则同时学习激励和抑制，因此可能违反戴尔的原理。我们显示，在相同的Hebbian-Bayesian学习规则下，当双束细胞上的锥体突触是可塑性时，保留了先前网络模型的功能列之间的学习能力和所产生的有效连通性。拟议的体系结构借鉴了关于双束细胞的实验证据，并通过塑性突触途径取代了具有不同刺激选择性的功能性列中的锥体细胞之间的反复抑制，从而有效解决了抑制和激发的双重学习问题。因此，我们表明，对微电路体系结构的最终更改提高了模型的生物学真实性，而不会影响模型的尖峰活动，基本操作和学习能力。