Title :
Silicon neuron dedicated to memristive spiking neural networks
Author :
Lecerf, Gwendal ; Tomas, Jean ; Boyn, Soren ; Girod, Stephanie ; Mangalore, Ashwin ; Grollier, J. ; Saighi, S.
Author_Institution :
IMS, Univ. Bordeaux, Talence, France
Abstract :
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.
Keywords :
elemental semiconductors; memristors; neural chips; neurophysiology; silicon; CCII; STDP; Si; analog neural network; current conveyor; excitatory synapses; ferroelectric memristor; inhibitory synapses; memristive spiking neural networks; plastic synapses; silicon neuron; spike generator; spike timing dependent plasticity; synaptic plasticity algorithm; synaptic weight updating principle; weight storage; Biological neural networks; Current measurement; Generators; Memristors; Neurons; Resistance; Silicon;
Conference_Titel :
Circuits and Systems (ISCAS), 2014 IEEE International Symposium on
Conference_Location :
Melbourne VIC
Print_ISBN :
978-1-4799-3431-7
DOI :
10.1109/ISCAS.2014.6865448
