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SYSTEMS & COGNITIVE NEUROSCIENCE

Neuroscience Visualizations with Editable Code

Build spike rasters, response curves, and connectivity-style summaries with one-click transitions into the full Analyze workflow.

Improved analysis flow

From spikes to summary

Prototype rasters and PSTH-style plots before moving to larger session datasets.

Model behavior clearly

Fit psychometric functions and annotate thresholds directly in figure code.

Bridge to related views

Connect to heatmap, PCA, and survival workflows for multimodal studies.

Spike Raster - Live Code

Use this as a starting point for trial-aligned event visualizations and stimulus response summaries.

Live Code Editor
Code EditorPython
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Learn by Experimenting

This is a safe playground for learning! Try changing:

  • • Colors: Modify color values to see different palettes
  • • Numbers: Adjust sizes, positions, or data ranges
  • • Labels: Update titles, axis names, or legends

Edit the code, run it, then open the full data visualization tool to continue with your own dataset.

Psychometric Function - Live Code

Tune threshold annotations and response model shape, then continue refinement in Analyze.

Live Code Editor
Code EditorPython
Loading editor...
Live Preview

Preparing preview

Running once automatically on first load

Learn by Experimenting

This is a safe playground for learning! Try changing:

  • • Colors: Modify color values to see different palettes
  • • Numbers: Adjust sizes, positions, or data ranges
  • • Labels: Update titles, axis names, or legends

Edit the code, run it, then open the full data visualization tool to continue with your own dataset.

Chart gallery

Related Neuroscience Chart Types

Bridge neural response analysis with matrix and dimensionality views

Browse all chart types →
Correlation heatmap with diverging color scale and coefficient annotations
Statistical•seaborn, matplotlib
From the chart gallery•Correlation analysis between variables

Heatmap

Represents data values as colors in a two-dimensional matrix format.

Sample code / prompt

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
    [1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
    [0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],

Ready to speed up neuroscience figure iteration?

Start from live code templates, then switch to Analyze for full dataset adaptation and export.

Start in Analyze