A Python toolbox for neural circuit parameter inference

doi:10.1038/s41540-025-00527-9

A Python toolbox for neural circuit parameter inference

2025-05-09

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Valero, Alejandro Orozco

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Víctor Rodríguez-González

Pablo Martínez‐Cañada *

DOI：10.1038/s41540-025-00527-9

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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.

Keywords:

LOCAL-FIELD POTENTIALS

ALZHEIMERS-DISEASE

DYNAMICS

MODELS

FRAMEWORK

NETWORKS

BALANCE

BRAIN

EEG

SIMULATION

A Python toolbox for neural circuit parameter inference

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