Synaptic electronics

Conventional computers excel in logic and accurate scientific calculations but make hard work of open ended problems that human brains handle easily. Even von Neumann—the mathematician and polymath who first developed the programming architecture that forms the basis of today's computers—was already looking to the brain for future developments before his death in 1957 [1]. Neuromorphic computing uses approaches that better mimic the working of the human brain. Recent developments in nanotechnology are now providing structures with very accommodating properties for neuromorphic approaches. This special issue, with guest editors James K Gimzewski and Dominique Vuillaume, is devoted to research at the serendipitous interface between the two disciplines. 'Synaptic electronics', looks at artificial devices with connections that demonstrate behaviour similar to synapses in the nervous system allowing a new and more powerful approach to computing. Synapses and connecting neurons respond differently to incident signals depending on the history of signals previously experienced, ultimately leading to short term and long term memory behaviour. The basic characteristics of a synapse can be replicated with around ten simple transistors. However with the human brain having around 1011 neurons and 1015 synapses, artificial neurons and synapses from basic transistors are unlikely to accommodate the scalability required. The discovery of nanoscale elements that function as 'memristors' has provided a key tool for the implementation of synaptic connections [2]. Leon Chua first developed the concept of the 'The memristor—the missing circuit element' in 1971 [3]. In this special issue he presents a tutorial describing how memristor research has fed into our understanding of synaptic behaviour and how they can be applied in information processing [4]. He also describes, 'The new principle of local activity, which uncovers a minuscule life-enabling "Goldilocks zone", dubbed the edge of chaos, where complex phenomena, including creativity and intelligence, may emerge'. Also in this issue R Stanley Williams and colleagues report results from simulations that demonstrate the potential for using Mott transistors as building blocks for scalable neuristor-based integrated circuits without transistors [5]. The scalability of neural chip designs is also tackled in the design reported by Narayan Srinivasa and colleagues in the US [6]. Meanwhile Carsten Timm and Massimiliano Di Ventra describe simulations of a molecular transistor in which electrons strongly coupled to a vibrational mode lead to a Franck–Condon (FC) blockade that mimics the spiking action potentials in synaptic memory behaviour [7]. The 'atomic switches' used to demonstrate synaptic behaviour by a collaboration of researchers in California and Japan also come under further scrutiny in this issue. James K Gimzewski and colleagues consider the difference between the behaviour of an atomic switch in isolation and in a network [8]. As the authors point out, 'The work presented represents steps in a unified approach of experimentation and theory of complex systems to make atomic switch networks a uniquely scalable platform for neuromorphic computing'. Researchers in Germany [9] and Sweden [10] also report on theoretical approaches to modelling networks of memristive elements and complementary resistive switches for synaptic devices. As Vincent Derycke and colleagues in France point out, 'Actual experimental demonstrations of neural network type circuits based on non-conventional/non-CMOS memory devices and displaying function learning capabilities remain very scarce'. They describe how their work using carbon nanotubes provides a rare demonstration of actual function learning with synapses based on nanoscale building blocks [11]. However, this is far from the only experimental work reported in this issue, others include: short-term memory of TiO2-based electrochemical capacitors [12]; a neuromorphic circuit composed of a nanoscale 1-kbit resistive random-access memory (RRAM) cross-point array of synapses and complementary metal–oxide–semiconductor (CMOS) neuron circuits [13]; a WO3−x-based nanoionics device from Masakazu Aono's group with a wide scale of reprogrammable memorization functions [14]; a new spike-timing dependent plasticity scheme based on a MOS transistor as a selector and a RRAM as a variable resistance device [15]; a new hybrid memristor-CMOS neuromorphic circuit [16]; and a photo-assisted atomic switch [17]. Synaptic electronics evidently has many emerging facets, and Duygu Kuzum, Shimeng Yu, and H-S Philip Wong in the US provide a review of the field, including the materials, devices and applications [18]. In embracing the expertise acquired over thousands of years of evolution, biomimetics and bio-inspired design is a common, smart approach to technological innovation. Yet in successfully mimicking the physiological mechanisms of the human mind synaptic electronics research has a potential impact that is arguably unprecedented. That the quirks and eccentricities recently unearthed in the behaviour of nanomaterials should lend themselves so accommodatingly to emulating synaptic functions promises some very exciting developments in the field, as the articles in this special issue emphasize. References [1] von Neumann J (ed) 2012 The Computer and the Brain 3rd edn (Yale: Yale University Press) [2] Strukov D B, Snider G S, Stewart D R and Williams R S 2008 The missing memristor found Nature 453 80–3 [3] Chua L O 1971 Memristor—the missing circuit element IEEE Trans. Circuit Theory 18 507–19 [4] Chua L O 2013 Memristor, Hodgkin–Huxley, and Edge of Chaos Nanotechnology 24 383001 [5] Pickett M D and Williams R S 2013 Phase transitions enable computational universality in neuristor-based cellular automata Nanotechnology 24 384002 [6] Cruz-Albrecht J M, Derosier T and Srinivasa N 2013 Scalable neural chip with synaptic electronics using CMOS integrated memristors Nanotechnology 24 384011 [7] Timm C and Di Ventra M 2013 Molecular neuron based on the Franck–Condon blockade Nanotechnology 24 384001 [8] Sillin H O, Aguilera R, Shieh H-H, Avizienis A V, Aono M, Stieg A Z and Gimzewski J K 2013 A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing Nanotechnology 24 384004 [9] Linn E, Menzel S, Ferch S and Waser R 2013 Compact modeling of CRS devices based on ECM cells for memory, logic and neuromorphic applications Nanotechnology 24 384008 [10] Konkoli Z and Wendin G 2013 A generic simulator for large networks of memristive elements Nanotechnology 24 384007 [11] Gacem K, Retrouvey J-M, Chabi D, Filoramo A, Zhao W, Klein J-O and Derycke V 2013 Neuromorphic function learning with carbon nanotube-based synapses Nanotechnology 24 384013 [12] Lim H, Kim I, Kim J-S, Hwang C S and Jeong D S 2013 Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold Nanotechnology 24 384005 [13] Park S, Noh J, Choo M-L, Sheri A M, Chang M, Kim Y-B, Kim C J, Jeon M, Lee B-G, Lee B H and Hwang H 2013 Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device Nanotechnology 24 384009 [14] Yang R, Terabe K, Yao Y, Tsuruoka T, Hasegawa T, Gimzewski J K and Aono M 2013 Synaptic plasticity and memory functions achieved in WO3−x-based nanoionics device by using principle of atomic switch operation Nanotechnology 24 384002 [15] Ambrogio S, Balatti S, Nardi F, Facchinetti S and Ielmini D 2013 Spike-timing dependent plasticity in a transistor-selected resistive switching memory Nanotechnology 24 384012 [16] Indiveria G, Linares-Barranco B, Legenstein R, Deligeorgis G and Prodromakise T 2013 Integration of nanoscale memristor synapses in neuromorphic computing architectures Nanotechnology 24 384010 [17] Hino T, Hasegawa T, Tanaka H, Tsuruoka T, Terabe K, Ogawa T and Aono M 2013 Volatile and nonvolatile selective switching of a photo-assited initialized atomic switch Nanotechnology 24 384006 [18] Kuzum D, Yu S and Wong H-S P 2013 Synaptic electronics: materials, devices and applications Nanotechnology 24 382001