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.
Create a Peristimulus Time Histogram with your data using AI — no coding required.
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.")
Opens the Analyze page with this code pre-loaded and ready to execute
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
Frequently asked questions
When should you use a peristimulus time histogram?
The peristimulus time histogram (PSTH) averages spike counts across trials in uniform time bins, converting discrete spike trains into a continuous firing-rate estimate. Neuroscientists use it to characterise the temporal profile of a neuron's response to a stimulus. Common applications include characterising excitatory and inhibitory responses in sensory cortex, comparing response latency across brain areas or pharmacological conditions, and detecting anticipatory activity before a behavioural cue.
Which Python libraries can create a peristimulus time histogram?
A peristimulus time histogram can be built in Python with matplotlib and numpy — matplotlib for precise control over axes, annotations, and journal styling and numpy. In Plotivy you describe the figure and it writes the matplotlib code for you.
Can I make a peristimulus time histogram without writing Python code?
Yes. Describe the peristimulus time histogram you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable matplotlib source, so nothing is locked in a black box.
What are best practices for a clear peristimulus time histogram?
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.
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
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