Experimental validation of an analog spiking neural network with STDP learning rule in CMOS technology-Reference-Cited by-同舟云学术

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Experimental validation of an analog spiking neural network with STDP learning rule in CMOS technology

Published:2022-10-26 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
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Author:

Polidori Elisabetta¹,Camisa Giovanni¹,Mesri Alireza¹,Ferrari Giorgio²,Polidori Cristina¹,Mastella Michele³,Prati Enrico⁴

Affiliation:

1. Politecnico di Milano,DEIB,Milano,Italy

2. Politecnico di Milano,Physics department,Milano,Italy

3. Zernike Institute for Advanced Materials and CogniGron University of Groningen,Groningen,The Netherlands

4. Università degli studi di Milano and IFN-CNR,Physics Department,Milano,Italy

Funder

Politecnico di Milano

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9967443/9967445/09967583.pdf?arnumber=9967583

Reference24 articles.

1. An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG;sharifshazileh;Nat Commun,2020

2. Biomimetic Intraneural Sensory Feedback Enhances Sensation Naturalness, Tactile Sensitivity, and Manual Dexterity in a Bidirectional Prosthesis

3. Spiking Neural Networks Hardware Implementations and Challenges

4. The SpiNNaker Project

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spike-based local synaptic plasticity: a survey of computational models and neuromorphic circuits;Neuromorphic Computing and Engineering;2023-11-17

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