Benoit R. Cottereau

Centre National de la Recherche Scientifique

Papers

Total Citations

H-Index

About

Benoit R. Cottereau is a leading researcher in computational neuroscience and computer vision, with a focus on biologically inspired models for depth perception. His work bridges the gap between neural coding principles and practical machine learning, particularly through spiking neural networks (SNNs). In his highly cited 2022 paper, "StereoSpike: Depth Learning With a Spiking Neural Network," Cottereau introduced a novel SNN architecture that solves stereo depth estimation—a critical task for autonomous vehicle navigation and robotic object manipulation. This work demonstrates how biologically plausible, event-driven computation can achieve competitive performance while offering energy efficiency advantages over traditional deep networks. With 44 citations, StereoSpike has already influenced both neuromorphic computing and computer vision communities. Cottereau’s broader contributions include advancing our understanding of how the brain processes binocular disparity, and his research continues to inspire new approaches to 3D vision that are both computationally efficient and neurally grounded. His work is essential reading for anyone interested in the intersection of biological vision, spiking neural networks, and real-world robotic perception.