Palash Ghosal

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About

Palash Ghosal is a researcher at the forefront of computational vision, specializing in spiking neural networks (SNNs) and their application to image analysis. His work bridges the gap between biological plausibility and practical computer vision, with a particular focus on developing energy-efficient, event-driven architectures. Ghosal’s most-cited paper, "Spiking Neural Network in Computer Vision: Techniques, Tools and Trends" (2023), has garnered 15 citations, establishing him as a key voice in this emerging field. In this comprehensive survey, he systematically maps the landscape of SNN-based vision—covering encoding schemes, learning algorithms, and hardware implementations—providing an essential resource for researchers seeking to leverage neuromorphic computing. Beyond this flagship work, Ghosal’s contributions extend to deep learning for medical imaging and remote sensing, where he explores novel architectures for feature extraction and classification. His research is notable for its interdisciplinary approach, combining insights from neuroscience, signal processing, and machine learning to push the boundaries of what SNNs can achieve in real-world vision tasks. For students and researchers, Ghosal’s work offers a clear roadmap into the exciting intersection of brain-inspired computing and computer vision.