LFP Trace
Chart overview
An LFP trace plots the continuous sub-threshold voltage signal recorded from a microelectrode as a function of time, reflecting the summed synaptic activity of nearby neuronal populations.
Key points
- Neuroscientists examine LFP traces to identify oscillatory rhythms such as theta, gamma, and slow-wave activity.
- These signals provide insight into network-level coordination and brain states that spike data alone cannot reveal.
Python Tutorial
How to create a lfp trace in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a multi-channel LFP trace plot from my electrophysiology data. Stack each channel vertically with a fixed offset, colour-code channels, mark stimulus onset with a vertical dashed line, and apply publication-quality journal styling with labelled amplitude scale bar."
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Python Code Example
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 2, 2000)
signal = np.sin(2 * np.pi * 8 * t) + 0.5 * np.sin(2 * np.pi * 40 * t) + np.random.normal(0, 0.5, len(t))
plt.figure(figsize=(12, 4))
plt.plot(t, signal, color='#1e293b', linewidth=1)
plt.title('Local Field Potential (LFP) Trace', fontsize=14, fontweight='bold', pad=15)
plt.xlabel('Time (s)', fontsize=12)
plt.ylabel('Voltage ($\mu V$)', fontsize=12)
plt.xlim(0, 2)
plt.tight_layout()
plt.savefig('plotivy-lfp-trace.png', dpi=150)
print("LFP trace generated successfully.")
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Console Output
LFP trace generated successfully.
Common Use Cases
- 1Identifying theta and gamma oscillations during spatial navigation tasks
- 2Detecting sharp-wave ripples in hippocampal sleep recordings
- 3Comparing oscillatory power before and after drug administration
- 4Monitoring depth profiles of slow-wave activity across cortical layers
Pro Tips
Apply a bandpass filter appropriate to the frequency band of interest before plotting
Stack multiple channels with a consistent vertical offset rather than overlapping them
Use a physical scale bar (e.g. 100 µV / 100 ms) instead of axis tick marks for publication
Mark experimental events with vertical lines or shaded rectangles aligned to the time axis
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for lfp-trace.
numpy
Useful in specialized workflows that complement core Python plotting libraries for lfp-trace analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.