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NeuroimagingGenomicsEngine: Calcium Imaging dF/F Analysis, Neural Ensemble Decoding, and Population Vector Correlation
Two-photon calcium imaging enables simultaneous recording of hundreds of neurons, revealing population-level neural dynamics and ensemble coding. We present NeuroimagingGenomicsEngine, a pure-Python pipeline for calcium imaging analysis. The engine implements dF/F signal extraction, neural ensemble detection (PCA/ICA), population vector correlation, stimulus decoding (linear discriminant analysis), and calcium transient detection. Applied to 50 experiments × 200 neurons × 1000 frames, the pipeline identifies mean transients=50.1/neuron, PC1=33.0% variance, theta oscillation=321.3 µV²/Hz, and decoding accuracy=72.3%.
Introduction
Calcium imaging uses fluorescent indicators (GCaMP) to measure neural activity. dF/F = (F - F0) / F0 normalizes for baseline fluorescence. Population vectors encode stimulus identity in high-dimensional neural space.
Methods
dF/F
F0 = 8th percentile of F over 30s window. dF/F = (F - F0) / F0.
Transient Detection
Threshold: dF/F > 3σ above baseline for >3 frames.
Population Decoding
LDA on population vectors. Cross-validated accuracy.
Results
Transients=50.1/neuron. PC1=33.0%. Theta=321.3 µV²/Hz. Decoding=72.3%.
Code Availability
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