Research

My works navigate human brain-inspired artificial intelligence, to emulate dynamical systems. Spatio temporal evolution of any system occurring in the physical world is an application. My implementations have been applied to signal processing, cancer biomarker prediction, particle trajectory prediction, tumor dynamics filtering, apnea prediction, and more. A detailed list of my publications and preprints are available on

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DBLP Archive • Software on GitHub

Selected Publications

“Weighted Hypoxemia Index: An adaptable method for quantifying hypoxemia severity.” Ankita Paul, et al. — PLOS ONE, 2025 Healthcare AI • Biomarkers

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“Learning in recurrent spiking neural networks with sparse full-FORCE training.” Ankita Paul, et al. — ICANN, 2024 Brain-inspired AI • Sequential Learning

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“Sparsity aware learning in feedback-driven differential recurrent neural networks.” Ankita Paul, et al. — ICANN, 2024 Recurrent Neural Networks • Sparsity

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“Data-Driven Learning of Aperiodic Nonlinear Dynamic Systems Using Spike-Based Reservoirs-in-Reservoir.” Ankita Paul, et al. — IJCNN, 2024 Reservoir Computing • Neural Networks

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“Learning in feedback-driven recurrent spiking neural networks using full-force training.” Ankita Paul, et al. — IJCNN, 2022 Spiking neural networks • Recurrent Neural Networks

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