Ponencia
Hardware Implementation of Convolutional STDP for On-line Visual Feature Learning
Autor/es | Yousefzadeh, Amirreza
Masquelier, T. Serrano Gotarredona, María Teresa Linares Barranco, Bernabé |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2017 |
Fecha de depósito | 2020-07-08 |
Publicado en |
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ISBN/ISSN | 2379-447X |
Resumen | We present a highly hardware friendly STDP (Spike Timing Dependent Plasticity) learning rule for training Spiking Convolutional Cores in Unsupervised mode and training Fully Connected Classifiers in Supervised ... We present a highly hardware friendly STDP (Spike Timing Dependent Plasticity) learning rule for training Spiking Convolutional Cores in Unsupervised mode and training Fully Connected Classifiers in Supervised Mode. Examples are given for a 2-layer Spiking Neural System which learns in real time features from visual scenes obtained with spiking DVS (Dynamic Vision Sensor) Cameras. |
Identificador del proyecto | 644096 “ECOMODE”
687299 “NEURAM3” TEC2012-37868-C04-01 (BIOSENSE) TIC-6091 (NANONEURO) |
Cita | Yousefzadeh, A., Masquelier, T., Serrano Gotarredona, M.T. y Linares Barranco, B. (2017). Hardware Implementation of Convolutional STDP for On-line Visual Feature Learning. En ISCAS 2017. IEEE International Symposium on Circuits and Systems (1-5), Baltimore (USA): IEEE. Institute of Electrical and Electronics Engineers. |
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