Spectral Properties of Local Field Potentials and Electroencephalograms as Indices for Changes in Neural Circuit Parameters

Electrical measurements of aggregate neural activity, such as local field potentials (LFPs) or electroencephalograms (EEGs), can capture oscillations of neural activity over a wide range of frequencies and are widely used to study brain function and dysfunction. However, relatively little is known about how to relate features of such aggregate neural recordings to the functional and anatomical configurations of the underlying neural circuits that produce them. An important neural circuit parameter which has profound effects on neural network dynamics and neural function is the ratio between excitation and inhibition (E:I), which has been found to be atypical in many neuropsychiatric conditions. Here we used simulations of recurrent networks of point-like leaky integrate-and-fire (LIF) neurons to study how to infer parameters such as the E:I ratio or the magnitude of the external input of the network from aggregate electrical measures. We used approximations (or proxies), validated in previous work, to generate realistic LFPs and EEGs from simulations of such networks. We computed different spectral features from simulated neural mass signals, such as the 1/f spectral power law or the Hurst exponent (H), and studied how these features changed when we changed the E:I ratio or the strength of the external input of the network model. We discuss how different spectral features of aggregate signals relate to the E:I ratio or the strength of the external input and outline our efforts to fit our model, in future work, to multiple measures extracted from empirical recordings of aggregate neural activity.