Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
Abstract Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of t...
Guardado en:
Autores principales: | G. Pedretti, V. Milo, S. Ambrogio, R. Carboni, S. Bianchi, A. Calderoni, N. Ramaswamy, A. S. Spinelli, D. Ielmini |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2017
|
Materias: | |
Acceso en línea: | https://doaj.org/article/1bedd02d99f6453c9fdb140384853231 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits
por: M. Prezioso, et al.
Publicado: (2018) -
Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
por: Qingxi Duan, et al.
Publicado: (2020) -
Energy‐Efficient Memristive Euclidean Distance Engine for Brain‐Inspired Competitive Learning
por: Houji Zhou, et al.
Publicado: (2021) -
BRAIN-INSPIRED SPIKING NEURAL NETWORKS FOR WI-FI BASED HUMAN ACTIVITY RECOGNITION
por: Yee Leong Tan, et al.
Publicado: (2021) -
SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
por: Fangxin Liu, et al.
Publicado: (2021)