Data from: Identifying anatomical origins of coexisting oscillations in the cortical microcircuit

Oscillations are omnipresent in neural population signals, like multi-unit recordings, EEG/MEG, and the local field potential. They have been linked to the population firing rate of neurons, with individual neurons firing in a close-to-irregular fashion at low rates. Using a combination of mean-field and linear response theory we predict the spectra generated in a layered microcircuit model of V1, composed of leaky integrate-and-fire neurons and based on connectivity compiled from anatomical and electrophysiological studies. The model exhibits low- and high-γ oscillations visible in all populations. Since locally generated frequencies are imposed onto other populations, the origin of the oscillations cannot be deduced from the spectra. We develop an universally applicable systematic approach that identifies the anatomical circuits underlying the generation of oscillations in a given network. Based on a theoretical reduction of the dynamics, we derive a sensitivity measure resulting in a frequency-dependent connectivity map that reveals connections crucial for the peak amplitude and frequency of the observed oscillations and identifies the minimal circuit generating a given frequency. The low-γ peak turns out to be generated in a sub-circuit located in layer 2/3 and 4, while the high-γ peak emerges from the inter-neurons in layer 4. Connections within and onto layer 5 are found to regulate slow rate fluctuations. We further demonstrate how small perturbations of the crucial connections have significant impact on the population spectra, while the impairment of other connections leaves the dynamics on the population level unaltered. The study uncovers connections where mechanisms controlling the spectra of the cortical microcircuit are most effective.神经群体信号中普遍存在振荡现象，这类信号涵盖多单元记录（multi-unit recordings）、脑电图（EEG）/脑磁图（MEG）以及局部场电位（local field potential）。振荡与神经元群体放电速率密切相关，而单个神经元通常以低速率近似无规则的方式放电。本研究结合平均场（mean-field）理论与线性响应（linear response）理论，对由漏积分放电（leaky integrate-and-fire）神经元构成、且连接组基于解剖学和电生理学研究构建的初级视觉皮层（V1）分层微环路模型所产生的频谱进行了预测。该模型的所有神经元群体均呈现低γ频段与高γ频段的振荡特征。由于局部产生的振荡频率会传递至其他神经元群体，因此无法仅通过频谱直接推断振荡的起源。为此，我们开发了一种普适性系统方法，可用于识别给定神经网络中产生振荡的解剖学环路。基于对动力学过程的理论简化，我们推导出一种灵敏度度量指标，由此得到频率依赖的连接图谱，该图谱可揭示对观测到的振荡峰值振幅与频率至关重要的连接，并能确定产生特定频率振荡的最小环路。研究发现，低γ频段峰值由位于2/3层与4层的子环路（sub-circuit）产生，而高γ频段峰值则源自4层的中间神经元（inter-neurons）。第五层内部及向第五层的连接被证实可调控缓慢的群体速率波动。我们进一步证明，对关键连接施加微小扰动会对群体频谱产生显著影响，而其他连接受损则不会改变群体层面的动力学特性。本研究明确了调控皮层微环路（cortical microcircuit）频谱的机制最为有效的连接靶点。