Peristimulus Time Histogram
Chart overview
The peristimulus time histogram (PSTH) averages spike counts across trials in uniform time bins, converting discrete spike trains into a continuous firing-rate estimate.
Key points
- Neuroscientists use it to characterise the temporal profile of a neuron's response to a stimulus.
- It quantifies response latency, peak firing rate, and post-excitatory inhibition in a single compact plot.
Python Tutorial
How to create a peristimulus time histogram in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Plot a Histogram in PythonExample Visualization
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"Create a peristimulus time histogram (PSTH) from my spike time data. Bin spikes into 10 ms windows, align to stimulus onset at t=0, normalise to spikes per second, shade the baseline period, and format for a journal-quality figure."
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Python Code Example
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)
spikes = np.random.normal(0.2, 0.1, 500)
spikes = spikes[(spikes > -0.2) & (spikes < 0.6)]
plt.figure(figsize=(10, 6))
plt.hist(spikes, bins=30, color='#3b82f6', edgecolor='black', alpha=0.8)
plt.axvline(0, color='red', linestyle='--', linewidth=2, label='Stimulus Onset')
plt.title('Peristimulus Time Histogram (PSTH)', fontsize=14, fontweight='bold', pad=20)
plt.xlabel('Time relative to stimulus (s)', fontsize=12)
plt.ylabel('Spike Count', fontsize=12)
plt.legend()
plt.tight_layout()
plt.savefig('plotivy-peristimulus-time-histogram.png', dpi=150)
print("PSTH generated successfully.")
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Console Output
PSTH generated successfully.
Common Use Cases
- 1Characterising excitatory and inhibitory responses in sensory cortex
- 2Comparing response latency across brain areas or pharmacological conditions
- 3Detecting anticipatory activity before a behavioural cue
- 4Quantifying adaptation and habituation of neural responses
Pro Tips
Choose bin width carefully: too wide blurs temporal precision, too narrow inflates noise
Smooth with a Gaussian kernel after binning to produce a continuous firing-rate estimate
Always show the pre-stimulus baseline period to establish the spontaneous rate
Normalise by trial count and bin width to report firing rate in Hz, not raw counts
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 peristimulus-time-histogram.
numpy
Useful in specialized workflows that complement core Python plotting libraries for peristimulus-time-histogram 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.