Associative Memory Realized by a Reconfigurable Memristive Hopfield Neural Network

Overview

Journal Nat Commun

Specialty Biology

Date 2015 Jun 26

PMID 26108993

Citations 33

Authors

S G Hu

Y Liu

Z Liu

T P Chen

J J Wang

Q Yu

L J Deng

Y Yin

Sumio Hosaka

Affiliations

Soon will be listed here.

Abstract

Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

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References

Hopfield J, Tank D . Computing with neural circuits: a model. Science. 1986; 233(4764):625-33. DOI: 10.1126/science.3755256. View

Alibart F, Zamanidoost E, Strukov D . Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun. 2013; 4:2072. DOI: 10.1038/ncomms3072. View

Li Y, Zhong Y, Xu L, Zhang J, Xu X, Sun H . Ultrafast synaptic events in a chalcogenide memristor. Sci Rep. 2013; 3:1619. PMC: 3619133. DOI: 10.1038/srep01619. View

Yang J, Strukov D, Stewart D . Memristive devices for computing. Nat Nanotechnol. 2012; 8(1):13-24. DOI: 10.1038/nnano.2012.240. View

Krzysteczko P, Munchenberger J, Schafers M, Reiss G, Thomas A . The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system. Adv Mater. 2012; 24(6):762-6. DOI: 10.1002/adma.201103723. View

Hopfield J . Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982; 79(8):2554-8. PMC: 346238. DOI: 10.1073/pnas.79.8.2554. View

Basheer I, Hajmeer M . Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000; 43(1):3-31. DOI: 10.1016/s0167-7012(00)00201-3. View

Pershin Y, Di Ventra M . Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 2010; 23(7):881-6. DOI: 10.1016/j.neunet.2010.05.001. View

Zhao L, Chen H, Wu S, Jiang Z, Yu S, Hou T . Multi-level control of conductive nano-filament evolution in HfO2 ReRAM by pulse-train operations. Nanoscale. 2014; 6(11):5698-702. DOI: 10.1039/c4nr00500g. View

10.

Hopfield J . Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci U S A. 1984; 81(10):3088-92. PMC: 345226. DOI: 10.1073/pnas.81.10.3088. View

11.

Kuzum D, Yu S, Wong H . Synaptic electronics: materials, devices and applications. Nanotechnology. 2013; 24(38):382001. DOI: 10.1088/0957-4484/24/38/382001. View

12.

Calka P, Martinez E, Delaye V, Lafond D, Audoit G, Mariolle D . Chemical and structural properties of conducting nanofilaments in TiN/HfO2-based resistive switching structures. Nanotechnology. 2013; 24(8):085706. DOI: 10.1088/0957-4484/24/8/085706. View

13.

Jo S, Chang T, Ebong I, Bhadviya B, Mazumder P, Lu W . Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010; 10(4):1297-301. DOI: 10.1021/nl904092h. View

14.

Strukov D, Snider G, Stewart D, Williams R . The missing memristor found. Nature. 2008; 453(7191):80-3. DOI: 10.1038/nature06932. View

15.

Hopfield J, Tank D . "Neural" computation of decisions in optimization problems. Biol Cybern. 1985; 52(3):141-52. DOI: 10.1007/BF00339943. View

16.

Eryilmaz S, Kuzum D, Jeyasingh R, Kim S, BrightSky M, Lam C . Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci. 2014; 8:205. PMC: 4106403. DOI: 10.3389/fnins.2014.00205. View

17.

Furber S, Temple S . Neural systems engineering. J R Soc Interface. 2007; 4(13):193-206. PMC: 2359843. DOI: 10.1098/rsif.2006.0177. View

18.

Kuzum D, Jeyasingh R, Lee B, Wong H . Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 2011; 12(5):2179-86. DOI: 10.1021/nl201040y. View

19.

Seo K, Kim I, Jung S, Jo M, Park S, Park J . Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. Nanotechnology. 2011; 22(25):254023. DOI: 10.1088/0957-4484/22/25/254023. View