Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks. Second, we show that structural and functional feature tuning of PV neurons emerges more clearly at the network level, i.e., that population-level measures identify functional and structural co-tuning of PV neurons that are not evident in pairwise individual-level measures. Finally, we show that such co-tuning can enhance network stability at the cost of reduced feature selectivity.

中文翻译：

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抑制性神经元中的协同调节作为一种由随机性、相关性和稳态可塑性介导的网络现象的出现

皮质兴奋性神经元显示出对刺激特征的清晰调节，但抑制性中间神经元的调节特性是模糊的。虽然抑制性神经元被认为在很大程度上未调谐，但一些研究表明，一些表达小白蛋白（PV）的神经元确实表现出特征选择性，并参与与锥体神经元的协同调谐子网络。在本研究中，我们首先使用平均场理论来证明控制 PV 与兴奋性神经元连接的突触动力学的稳态可塑性、影响 PV 神经元的兴奋性突触后电位的异质性以及来自层的共享相关输入的组合图4导致光伏子网的功能和结构自组织。其次，我们表明PV神经元的结构和功能特征调整在网络水平上更加清晰地出现，即群体水平测量识别PV神经元的功能和结构协同调整，这在成对个体水平测量中并不明显。最后，我们表明这种协同调整可以增强网络稳定性，但代价是降低特征选择性。