Inferring Neural Circuit Interactions and Neuromodulation from Local Field Potential and Electroencephalogram Measures

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, being aggregate signals that lack cellular resolution, these signals are not easy to interpret directly in terms of neural functions. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important both for neuroscience and clinical applications. Over the years we have developed tools based on the combination of neural network modelling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. The purpose of this paper, which accompanies an Invited Plenary Lecture in this conference, is to review the main tools that we have developed to estimate neural parameters from mass signals, and to outline future challenges and directions for developing computational tools to invert aggregate neural signals in terms of neural circuit parameters.