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Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

Opened Access Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

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Autor: Zhao, Bo
Ding, R.
Chen, Shoushun
Linares Barranco, Bernabé
Fecha: 2015
Publicado en: IEEE Transactions on Neural Networks and Learning Systems, 26 (9), 1963-1978.
Tipo de documento: Artículo
Resumen: This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system’s most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into...
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Cita: Zhao, B., Ding, R., Chen, S. y Linares Barranco, B. (2015). Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network. IEEE Transactions on Neural Networks and Learning Systems, 26 (9), 1963-1978.
Tamaño: 1.919Mb
Formato: PDF

URI: https://hdl.handle.net/11441/74311

DOI: 10.1109/TNNLS.2014.2362542

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