A Python toolbox for neural circuit parameter inference Orozco Valero, Alejandro Rodríguez-González, Víctor Montobbio, Noemi Casal, Miguel A. Tlaie, Alejandro Pelayo Valle, Francisco José Morillas Gutiérrez, Christian Agustín Poza, Jesús Gómez, Carlos Martínez-Cañada, Pablo This study was supported by grants PID2022-139055OA-I00, PID2022- 137461NB-C31, and PID2022-138286NB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way ofmaking Europe”; by “Junta de Andalucía” - Postdoctoral Fellowship Program PAIDI 2021; and by “CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain” through “Instituto de Salud Carlos III” co-funded with ERDF funds. Computational research tools have reached a level of maturity that enables efficient simulation of neural activity across diverse scales. Concurrently, experimental neuroscience is experiencing an unprecedented scale of data generation. Despite these advancements, our understanding of the precise mechanistic relationship between neural recordings and key aspects of neural activity remains insufficient, including which specific features of electrophysiological population dynamics (i.e., putative biomarkers) best reflect properties of the underlying microcircuit configuration. We present ncpi, an open-source Python toolbox that serves as an all-in-one solution, effectively integrating well-established methods for both forward and inverse modeling of extracellular signals based on single-neuron network model simulations. Our tool serves as a benchmarking resource for model-driven interpretation of electrophysiological data and the evaluation of candidate biomarkers that plausibly index changes in neural circuit parameters. Using mouse LFP data and human EEG recordings, we demonstrate the potential of ncpi to uncover imbalances in neural circuit parameters during brain development and in Alzheimer’s Disease. 2025-06-30T10:49:29Z 2025-06-30T10:49:29Z 2025 journal article Orozco Valero, A., Rodríguez-González, V., Montobbio, N. et al. A Python toolbox for neural circuit parameter inference. npj Syst Biol Appl 11, 45 (2025). [DOI: 10.1038/s41540-025-00527-9] https://hdl.handle.net/10481/104978 10.1038/s41540-025-00527-9 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Nature Publishing Group