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LFPy
Python library for simulating extracellular potentials and related brain signals from multicompartment neuron models
Our View
LFPy connects multicompartment NEURON models with predictions of extracellular potentials and other observable signals. It is worth exploring when an analysis needs to relate cellular or network activity to simulated electrode measurements while keeping the biophysical assumptions explicit.
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Teaching — Recorded in a scheduled local-field-potential offering; consult the dated event link for availability.
1 topics with recorded teaching · 0 past offerings · 1 upcoming offerings
Related themes: Neural Data Analysis
Related topics: Local Field Potential (LFP) Analysis
Common use cases
- Calculate extracellular potentials from detailed neuron models
- Simulate local field potentials and extracellular spikes
- Model signals from single cells and recurrent neural networks
- Predict ECoG, EEG, and MEG signals from simulated neural activity
Getting started
LFPy is a Python library for calculating extracellular potentials from multicompartment neuron models. It uses the NEURON simulator to model cellular and network activity and provides methods for predicting signals measured outside the modeled cells.
Why LFPy?
- Biophysical simulations: Relate transmembrane currents in detailed neuron models to extracellular signals
- Multiple signal types: Model extracellular spikes, LFP, ECoG, EEG, and MEG signals
- Single cells and networks: Work with individual multicompartment cells or recurrent neural networks
- Electrode modeling: Evaluate simulated signals at configurable recording locations
- Python workflows: Configure simulations, inspect results, and create visualizations in Python
Installation
pixi add lfpy
# or
pip install LFPy
LFPy relies on the NEURON simulator. Follow the official documentation for current dependency and installation guidance.
Modeling Considerations
Extracellular predictions depend on the neuron model, electrode geometry, tissue-conductivity assumptions, and choice of forward model. Record these choices and validate them for the spatial and temporal scales relevant to the research question.