Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures Martínez-Cañada, Pablo Noei, Shahryar Panzeri, Stefano Local field potential (LFP) Electroencephalogram (EEG) Neural oscillation This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 893825-ESNECO to P.M.C, the NIH Brain Initiative (Grants U19NS107464 and NS108410 to S.P.), the Simons Foundation (SFARI Explorer 602849 to S.P.), and by the EU FESR-FSE PON “Ricerca & Innovazione 2014-2020”. Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, these aggregate signals lack cellular resolution and thus are not easy to be interpreted directly in terms of parameters of neural microcircuits. 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 for both neuroscientific and clinical applications. Over the years, we have developed tools based on neural network modeling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. This review article gives an overview of the main computational tools that we have developed and employed to invert LFPs and EEGs in terms of circuit-level neural phenomena, and outlines future challenges and directions for future research. 2026-02-10T11:55:02Z 2026-02-10T11:55:02Z 2021 journal article Martínez-Cañada, P.; Noei, S. y Panzeri, S. (2021). Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures. Brain Informatics (2021) 8:27 https://doi.org/10.1186/s40708-021-00148-y 2198-4026 2198-4018 https://hdl.handle.net/10481/110823 10.1186/s40708-021-00148-y eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Springer