  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-gamma 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-gamma peak turns out to be generated in a sub-circuit
located in layer 2/3 and 4, while the high-gamma 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.

Neurons and Cognition