Title :
Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks
Author :
Lecerf, Gwendal ; Tomas, Jean ; Saighi, S.
Author_Institution :
IMS, Univ. Bordeaux, Talence, France
Abstract :
Neuromorphic chips are composed of silicon neurons, synapses and memories for synaptic weight. Moreover we can find a fourth part dedicated to synaptic plasticity algorithm. Even though we can find some low-power silicon neurons, the power consumption reduction of synapses, memories and plasticity algorithm is not enough explored. Since memristor coming-out in 2008, neuromorphic designers investigate the possibility of using memristors as plastic synapses due to their intrinsic property of plasticity. This nanocomponent gathers the function of synapse, the weight storage and the plasticity. So far, the proposed solutions cannot manage both excitatory and inhibitory memristive synapses with one single design. In this paper we will present an elegant solution based on current conveyor (CCII) for driving memristor as excitatory or inhibitory synapses following the neural network implementation.
Keywords :
current conveyors; elemental semiconductors; neural nets; silicon; Si; current conveyor; excitatory memristive synapses; inhibitory memristive synapses; low-power silicon neurons; nanocomponent; neural network implementation; neuromorphic chips; plastic synapses; power consumption reduction; spiking neural networks; synaptic plasticity algorithm; synaptic weight; weight storage; Biological neural networks; Hardware; Memristors; Neurons; Power demand; Resistance; Silicon;
Conference_Titel :
Circuits and Systems (ISCAS), 2013 IEEE International Symposium on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-5760-9
DOI :
10.1109/ISCAS.2013.6572171