Silicon Neuron dedicated to Memristive Spiking Neural Networks

被引：0

作者：

Lecerf, Gwendal ^{[1
]}

Tomas, Jean ^{[1
]}

Boyn, Soeren ^{[2
,3
]}

Girod, Stephanie ^{[2
,3
]}

Mangalore, Ashwin ^{[1
]}

Grollier, Julie ^{[2
,3
]}

Saighi, Sylvain ^{[1
]}

机构：

[1] Univ Bordeaux, IMS, UMR 5218, F-33400 Talence, France

[2] Unite Mixte Phys CNRS Thales, F-91767 Palaiseau, France

[3] Univ Paris 11, F-91405 Orsay, France

来源：

2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2014年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Since memristor came out in 2008, neuromorphic designers investigated the possibility of using memristors as plastic synapses due to their intrinsic properties of plasticity and weight storage. In this paper we will present a silicon neuron compatible with memristive synapses in order to build analog neural network. This neuron mainly includes current conveyor (CCII) for driving memristor as excitatory or inhibitory synapses and spike generator whose waveform is dedicated to synaptic plasticity algorithm based on Spike Timing Dependent Plasticity (STDP). This silicon neuron has been fabricated, characterized and finally connected with a ferroelectric memristor to validate the synaptic weight updating principle.

引用

页码：1568 / 1571

页数：4

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