Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification
Fig 9
Comparison of LKCNN against conventional baselines and robustness to spike-like corruption.
(a) Classification accuracy (%) of LKCNN versus two conventional classifiers, namely, linear-kernel SVM and Random Forest (see legend at the top of the figure) on noiseless synthetic trajectories, noisy synthetic trajectories, and experimental Ca2+ traces. SVM and RF operate on FFT-based features of the trajectories. (b) Accuracy drop (%) after adding sparse, large-amplitude impulsive noise to the synthetic test trajectories. Accuracy drop is computed relative to the corresponding uncorrupted test set.