Welcome to ncpi!
ncpi is a Python package for model-based inference of neural circuit parameters
from population-level electrophysiological recordings, such as LFP, ECoG, MEG, and EEG.
ncpi provides a rapid, reproducible, and robust framework for estimating the most
probable neural circuit parameters associated with an empirical observation, streamlining
traditionally complex workflows into a minimal amount of code.
One environment, from simulated neural circuits to real data
Translate mechanistic hypotheses into simulated neural activity, multiscale field potentials, interpretable biomarkers, and model-based inference through a unified, end-to-end computational workflow.
ncpi connects brain simulation, forward modelling of field potentials, feature
extraction, and neural circuit parameter inference in a modular and reproducible computational
framework. Use only the components you need, or combine them into a complete pipeline that links
computational models with empirical measurements across recording scales.
Simulate and observe
Generate synthetic neural activity and transform it into CDM, LFP, EEG, or MEG observables.
Extract and infer
Extract interpretable biomarkers, train inverse models, compute predictions, and analyze results.
Choose how you work
Build workflows in pure Python, develop interactively in Jupyter, or use the guided WebUI.
Run wherever research happens
Move reproducible workflows from local workstations to shared server infrastructure.
Start Here
If this is your first time with ncpi, start with
Installation to get everything set up.
Then jump into the Tutorials to try guided WebUI
or Jupyter workflows.
When you need method-level details, the API page is ready.