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Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

Opened Access Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

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Autor: Liu, Qian
Pineda García, Garibaldi
Stromatias, Evangelos
Serrano Gotarredona, María Teresa
Furber, Steve B.
Departamento: Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Fecha: 2016
Publicado en: Frontiers in Neuroscience, 10, 496-.
Tipo de documento: Artículo
Resumen: Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-b...
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Cita: Liu, Q., Pineda García, G., Stromatias, E., Serrano Gotarredona, M.T. y Furber, S.B. (2016). Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation. Frontiers in Neuroscience, 10, 496-.
Tamaño: 3.661Mb
Formato: PDF

URI: https://hdl.handle.net/11441/73758

DOI: 10.3389/fnins.2016.00496

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