1
|
Bile A, Tari H, Pepino R, Nabizada A, Fazio E. Photorefraction Simulates Well the Plasticity of Neural Synaptic Connections. Biomimetics (Basel) 2024; 9:231. [PMID: 38667243 PMCID: PMC11047923 DOI: 10.3390/biomimetics9040231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
In recent years, the need for systems capable of achieving the dynamic learning and information storage efficiency of the biological brain has led to the emergence of neuromorphic research. In particular, neuromorphic optics was born with the idea of reproducing the functional and structural properties of the biological brain. In this context, solitonic neuromorphic research has demonstrated the ability to reproduce dynamic and plastic structures capable of learning and storing through conformational changes in the network. In this paper, we demonstrate that solitonic neural networks are capable of mimicking the functional behaviour of biological neural tissue, in terms of synaptic formation procedures and dynamic reinforcement.
Collapse
Affiliation(s)
- Alessandro Bile
- Department of Fundamental and Applied Sciences for Engineering, Sapienza Università di Roma, Via Scarpa 16, 00161 Roma, Italy; (H.T.); (R.P.); (A.N.); (E.F.)
| | | | | | | | | |
Collapse
|
2
|
Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS Appl Mater Interfaces 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
Collapse
Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| |
Collapse
|
3
|
Schegolev AE, Klenov NV, Gubochkin GI, Kupriyanov MY, Soloviev II. Bio-Inspired Design of Superconducting Spiking Neuron and Synapse. Nanomaterials (Basel) 2023; 13:2101. [PMID: 37513112 PMCID: PMC10383304 DOI: 10.3390/nano13142101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.
Collapse
Affiliation(s)
- Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
| | - Georgy I Gubochkin
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Russian Quantum Center, 100 Novaya Street, Skolkovo, 143025 Moscow, Russia
| | - Mikhail Yu Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
| |
Collapse
|
4
|
Tominov RV, Vakulov ZE, Avilov VI, Shikhovtsov IA, Varganov VI, Kazantsev VB, Gupta LR, Prakash C, Smirnov VA. Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications. Nanomaterials (Basel) 2023; 13:nano13101583. [PMID: 37242000 DOI: 10.3390/nano13101583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023]
Abstract
This paper proposes two different approaches to studying resistive switching of oxide thin films using scratching probe nanolithography of atomic force microscopy (AFM). These approaches allow us to assess the effects of memristor size and top-contact thickness on resistive switching. For that purpose, we investigated scratching probe nanolithography regimes using the Taguchi method, which is known as a reliable method for improving the reliability of the result. The AFM parameters, including normal load, scratch distance, probe speed, and probe direction, are optimized on the photoresist thin film by the Taguchi method. As a result, the pinholes with diameter ranged from 25.4 ± 2.2 nm to 85.1 ± 6.3 nm, and the groove array with a depth of 40.5 ± 3.7 nm and a roughness at the bottom of less than a few nanometers was formed. Then, based on the Si/TiN/ZnO/photoresist structures, we fabricated and investigated memristors with different spot sizes and TiN top contact thickness. As a result, the HRS/LRS ratio, USET, and ILRS are well controlled for a memristor size from 27 nm to 83 nm and ranged from ~8 to ~128, from 1.4 ± 0.1 V to 1.8 ± 0.2 V, and from (1.7 ± 0.2) × 10-10 A to (4.2 ± 0.6) × 10-9 A, respectively. Furthermore, the HRS/LRS ratio and USET are well controlled at a TiN top contact thickness from 8.3 ± 1.1 nm to 32.4 ± 4.2 nm and ranged from ~22 to ~188 and from 1.15 ± 0.05 V to 1.62 ± 0.06 V, respectively. The results can be used in the engineering and manufacturing of memristive structures for neuromorphic applications of brain-inspired artificial intelligence systems.
Collapse
Affiliation(s)
- Roman V Tominov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Department of Radioelectronics and Nanoelectronics, Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Zakhar E Vakulov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Vadim I Avilov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Ivan A Shikhovtsov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Vadim I Varganov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Victor B Kazantsev
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
| | - Lovi Raj Gupta
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Division of Research and Development, Lovely Professional University, Phagwara 144411, Panjab, India
| | - Chander Prakash
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Panjab, India
| | - Vladimir A Smirnov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Department of Radioelectronics and Nanoelectronics, Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| |
Collapse
|
5
|
Humbert V, El Hage R, Krieger G, Sanchez‐Santolino G, Sander A, Collin S, Trastoy J, Briatico J, Santamaria J, Preziosi D, Villegas JE. An Oxygen Vacancy Memristor Ruled by Electron Correlations. Adv Sci (Weinh) 2022; 9:e2201753. [PMID: 35901494 PMCID: PMC9507366 DOI: 10.1002/advs.202201753] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/01/2022] [Indexed: 06/15/2023]
Abstract
Resistive switching effects offer new opportunities in the field of conventional memories as well as in the booming area of neuromorphic computing. Here the authors demonstrate memristive switching effects produced by a redox-driven oxygen exchange in tunnel junctions based on NdNiO3 , a strongly correlated electron system characterized by the presence of a metal-to-insulator transition (MIT). Strikingly, a strong interplay exists between the MIT and the redox mechanism, which on the one hand modifies the MIT itself, and on the other hand radically affects the tunnel resistance switching and the resistance states' lifetime. That results in a very unique temperature behavior and endows the junctions with multiple degrees of freedom. The obtained results bring up fundamental questions on the interplay between electronic correlations and the creation and mobility of oxygen vacancies in nickelates, opening a new avenue toward mimicking neuromorphic functions by exploiting the electric-field control of correlated states.
Collapse
Affiliation(s)
- Vincent Humbert
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Ralph El Hage
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | | | - Gabriel Sanchez‐Santolino
- Grupo de Física de Materiales ComplejosDpt. Física de MaterialesUniversidad Complutense de MadridMadrid28040Spain
| | - Anke Sander
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Sophie Collin
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Juan Trastoy
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Javier Briatico
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Jacobo Santamaria
- Grupo de Física de Materiales ComplejosDpt. Física de MaterialesUniversidad Complutense de MadridMadrid28040Spain
| | | | - Javier E. Villegas
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| |
Collapse
|
6
|
Anand A, Sen S, Roy K. Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping. Front Comput Neurosci 2021; 15:609721. [PMID: 34504416 PMCID: PMC8421725 DOI: 10.3389/fncom.2021.609721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity, or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a non-linear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the TensorFlow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.
Collapse
Affiliation(s)
- Aditi Anand
- West Lafayette Junior/Senior High School, West Lafayette, IN, United States.,Center for Brain-Inspired Computing, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Sanchari Sen
- Center for Brain-Inspired Computing, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.,IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
| | - Kaushik Roy
- Center for Brain-Inspired Computing, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
7
|
Zhang W, Al-Khalidi A, Figueiredo J, Al-Taai QRA, Wasige E, Hadfield RH. Analysis of Excitability in Resonant Tunneling Diode-Photodetectors. Nanomaterials (Basel) 2021; 11:1590. [PMID: 34204375 PMCID: PMC8234959 DOI: 10.3390/nano11061590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/03/2021] [Accepted: 06/16/2021] [Indexed: 11/24/2022]
Abstract
We investigate the dynamic behaviour of resonant tunneling diode-photodetectors (RTD-PDs) in which the excitability can be activated by either electrical noise or optical signals. In both cases, we find the characteristics of the stochastic spiking behavior are not only dependent on the biasing positions but also controlled by the intensity of the input perturbations. Additionally, we explore the ability of RTD-PDs to perform optical signal transmission and neuromorphic spike generation simultaneously. These versatile functions indicate the possibility of making use of RTD-PDs for innovative applications, such as optoelectronic neuromorphic circuits for spike-encoded signaling and data processing.
Collapse
Affiliation(s)
- Weikang Zhang
- Division of Electronic and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK; (A.A.-K.); (Q.R.A.A.-T.); (E.W.); (R.H.H.)
| | - Abdullah Al-Khalidi
- Division of Electronic and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK; (A.A.-K.); (Q.R.A.A.-T.); (E.W.); (R.H.H.)
| | - José Figueiredo
- Centra-Ciências and Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal;
| | - Qusay Raghib Ali Al-Taai
- Division of Electronic and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK; (A.A.-K.); (Q.R.A.A.-T.); (E.W.); (R.H.H.)
| | - Edward Wasige
- Division of Electronic and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK; (A.A.-K.); (Q.R.A.A.-T.); (E.W.); (R.H.H.)
| | - Robert H. Hadfield
- Division of Electronic and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, UK; (A.A.-K.); (Q.R.A.A.-T.); (E.W.); (R.H.H.)
| |
Collapse
|
8
|
Tominov RV, Vakulov ZE, Avilov VI, Khakhulin DA, Polupanov NV, Smirnov VA, Ageev OA. The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films. Molecules 2020; 26:E118. [PMID: 33383898 PMCID: PMC7795261 DOI: 10.3390/molecules26010118] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/22/2020] [Accepted: 12/25/2020] [Indexed: 11/23/2022] Open
Abstract
We have experimentally studied the influence of pulsed laser deposition parameters on the morphological and electrophysical parameters of vanadium oxide films. It is shown that an increase in the number of laser pulses from 10,000 to 60,000 and an oxygen pressure from 3 × 10-4 Torr to 3 × 10-2 Torr makes it possible to form vanadium oxide films with a thickness from 22.3 ± 4.4 nm to 131.7 ± 14.4 nm, a surface roughness from 7.8 ± 1.1 nm to 37.1 ± 11.2 nm, electron concentration from (0.32 ± 0.07) × 1017 cm-3 to (42.64 ± 4.46) × 1017 cm-3, electron mobility from 0.25 ± 0.03 cm2/(V·s) to 7.12 ± 1.32 cm2/(V·s), and resistivity from 6.32 ± 2.21 Ω·cm to 723.74 ± 89.21 Ω·cm. The regimes at which vanadium oxide films with a thickness of 22.3 ± 4.4 nm, a roughness of 7.8 ± 1.1 nm, and a resistivity of 6.32 ± 2.21 Ω·cm are obtained for their potential use in the fabrication of ReRAM neuromorphic systems. It is shown that a 22.3 ± 4.4 nm thick vanadium oxide film has the bipolar effect of resistive switching. The resistance in the high state was (89.42 ± 32.37) × 106 Ω, the resistance in the low state was equal to (6.34 ± 2.34) × 103 Ω, and the ratio RHRS/RLRS was about 14,104. The results can be used in the manufacture of a new generation of micro- and nanoelectronics elements to create ReRAM of neuromorphic systems based on vanadium oxide thin films.
Collapse
Affiliation(s)
- Roman V. Tominov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Zakhar E. Vakulov
- Federal Research Centre The Southern Scientific Centre of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russia;
| | - Vadim I. Avilov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Daniil A. Khakhulin
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Nikita V. Polupanov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Vladimir A. Smirnov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Oleg A. Ageev
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| |
Collapse
|
9
|
Tominov RV, Vakulov ZE, Avilov VI, Khakhulin DA, Fedotov AA, Zamburg EG, Smirnov VA, Ageev OA. Synthesis and Memristor Effect of a Forming-Free ZnO Nanocrystalline Films. Nanomaterials (Basel) 2020; 10:E1007. [PMID: 32466144 PMCID: PMC7280973 DOI: 10.3390/nano10051007] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/11/2020] [Accepted: 05/21/2020] [Indexed: 11/16/2022]
Abstract
We experimentally investigated the effect of post-growth annealing on the morphological, structural, and electrophysical parameters of nanocrystalline ZnO films fabricated by pulsed laser deposition. The influence of post-growth annealing modes on the electroforming voltage and the resistive switching effect in ZnO nanocrystalline films is investigated. We demonstrated that nanocrystalline zinc oxide films, fabricated at certain regimes, show the electroforming-free resistive switching. It was shown, that the forming-free nanocrystalline ZnO film demonstrated a resistive switching effect and switched at a voltage 1.9 ± 0.2 V from 62.42 ± 6.47 (RHRS) to 0.83 ± 0.06 kΩ (RLRS). The influence of ZnO surface morphology on the resistive switching effect is experimentally investigated. It was shown, that the ZnO nanocrystalline film exhibits a stable resistive switching effect, which is weakly dependent on its nanoscale structure. The influence of technological parameters on the resistive switching effect in a forming-free ZnO nanocrystalline film is investigated. The results can be used for fabrication of new-generation micro- and nanoelectronics elements, including random resistive memory (ReRAM) elements for neuromorphic structures based on forming-free ZnO nanocrystalline films.
Collapse
Affiliation(s)
- Roman V. Tominov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| | - Zakhar E. Vakulov
- Federal Research Centre, The Southern Scientific Centre of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russia;
| | - Vadim I. Avilov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| | - Daniil A. Khakhulin
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| | - Aleksandr A. Fedotov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| | - Evgeny G. Zamburg
- Department of Electrical & Computer Engineering, National University of Singapore, Singapore 117582, Singapore;
| | - Vladimir A. Smirnov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| | - Oleg A. Ageev
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (A.A.F.); (O.A.A.)
| |
Collapse
|
10
|
Camuñas-Mesa LA, Linares-Barranco B, Serrano-Gotarredona T. Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations. Materials (Basel) 2019; 12:E2745. [PMID: 31461877 PMCID: PMC6747825 DOI: 10.3390/ma12172745] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/02/2019] [Accepted: 08/10/2019] [Indexed: 11/17/2022]
Abstract
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal-Oxide-Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.
Collapse
Affiliation(s)
- Luis A Camuñas-Mesa
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Sevilla, Spain.
| | - Bernabé Linares-Barranco
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Sevilla, Spain
| | - Teresa Serrano-Gotarredona
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Sevilla, Spain
| |
Collapse
|
11
|
Wlaźlak E, Marzec M, Zawal P, Szaciłowski K. Memristor in a Reservoir System-Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI 2. ACS Appl Mater Interfaces 2019; 11:17009-17018. [PMID: 30986023 DOI: 10.1021/acsami.9b01841] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Lead halides in an asymmetric layered structure form memristive devices which are controlled by the electronic structure of the PbX2|metal interface. In this paper, we explain the mechanism that stands behind the I- V pinched hysteresis loop of the device and shortly present its synaptic-like plasticity (spike-timing-dependent plasticity and spike-rate-dependent plasticity) and nonvolatile memory effects. This memristive element was incorporated into a reservoir system, in particular, the echo-state network with delayed feedback, which exhibits brain-like recurrent behavior and demonstrates metaplasticity as one of the available learning mechanisms. It can serve as a classification system that classifies input signals according to their amplitude.
Collapse
Affiliation(s)
- E Wlaźlak
- Faculty of Chemistry , Jagiellonian University , ul. Gronostajowa 2 , 30-060 Kraków , Poland
| | | | | | | |
Collapse
|
12
|
Barrios-Avilés J, Rosado-Muñoz A, Medus LD, Bataller-Mompeán M, Guerrero-Martínez JF. Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm. Sensors (Basel) 2018; 18:E4122. [PMID: 30477237 DOI: 10.3390/s18124122] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 11/21/2018] [Accepted: 11/22/2018] [Indexed: 11/28/2022]
Abstract
Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a “transparent mode” to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).
Collapse
|
13
|
Yousefzadeh A, Stromatias E, Soto M, Serrano-Gotarredona T, Linares-Barranco B. On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights. Front Neurosci 2018; 12:665. [PMID: 30374283 PMCID: PMC6196279 DOI: 10.3389/fnins.2018.00665] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 09/04/2018] [Indexed: 11/21/2022] Open
Abstract
In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required memory resources, but also the computing, communication, and energy resources. When it comes to hardware engineering, a key question is always to find the minimum number of necessary bits to keep the neurocomputational system working satisfactorily. Here we present some techniques and results obtained when limiting synaptic weights to 1-bit precision, applied to a Spike-Timing-Dependent-Plasticity (STDP) learning rule in Spiking Neural Networks (SNN). We first illustrate the 1-bit synapses STDP operation by replicating a classical biological experiment on visual orientation tuning, using a simple four neuron setup. After this, we apply 1-bit STDP learning to the hidden feature extraction layer of a 2-layer system, where for the second (and output) layer we use already reported SNN classifiers. The systems are tested on two spiking datasets: a Dynamic Vision Sensor (DVS) recorded poker card symbols dataset and a Poisson-distributed spike representation MNIST dataset version. Tests are performed using the in-house MegaSim event-driven behavioral simulator and by implementing the systems on FPGA (Field Programmable Gate Array) hardware.
Collapse
Affiliation(s)
- Amirreza Yousefzadeh
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, Sevilla, Spain
| | - Evangelos Stromatias
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, Sevilla, Spain
| | - Miguel Soto
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, Sevilla, Spain
| | | | - Bernabé Linares-Barranco
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, Sevilla, Spain
| |
Collapse
|
14
|
Kim S, Choi B, Lim M, Kim Y, Kim HD, Choi SJ. Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification. Small 2018; 14:e1800521. [PMID: 30009414 DOI: 10.1002/smll.201800521] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/07/2018] [Indexed: 06/08/2023]
Abstract
For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy-efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device-to-system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction.
Collapse
Affiliation(s)
- Sungho Kim
- Department of Electrical Engineering, Sejong University, Seoul, 05006, South Korea
| | - Bongsik Choi
- School of Electrical Engineering, Kookmin University, Seoul, 02707, South Korea
| | - Meehyun Lim
- Mechatronics R&D Center, Samsung Electronics, Gyonggi-do, 18448, South Korea
| | - Yeamin Kim
- School of Electrical Engineering, Kookmin University, Seoul, 02707, South Korea
| | - Hee-Dong Kim
- Department of Electrical Engineering, Sejong University, Seoul, 05006, South Korea
| | - Sung-Jin Choi
- School of Electrical Engineering, Kookmin University, Seoul, 02707, South Korea
| |
Collapse
|
15
|
Zarudnyi K, Mehonic A, Montesi L, Buckwell M, Hudziak S, Kenyon AJ. Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices. Front Neurosci 2018; 12:57. [PMID: 29472837 PMCID: PMC5809439 DOI: 10.3389/fnins.2018.00057] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 01/23/2018] [Indexed: 11/13/2022] Open
Abstract
Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.
Collapse
Affiliation(s)
- Konstantin Zarudnyi
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| | - Luca Montesi
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| | - Mark Buckwell
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| | - Stephen Hudziak
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| | - Anthony J Kenyon
- Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
| |
Collapse
|
16
|
Hansen M, Zahari F, Ziegler M, Kohlstedt H. Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition. Front Neurosci 2017; 11:91. [PMID: 28293164 PMCID: PMC5328953 DOI: 10.3389/fnins.2017.00091] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 02/10/2017] [Indexed: 11/13/2022] Open
Abstract
The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al2O3/Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I-V non-linearity might avoid the need for selector devices in crossbar array structures.
Collapse
Affiliation(s)
| | | | - Martin Ziegler
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu KielKiel, Germany
| | | |
Collapse
|
17
|
Qian C, Sun J, Kong LA, Gou G, Yang J, He J, Gao Y, Wan Q. Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors. ACS Appl Mater Interfaces 2016; 8:26169-26175. [PMID: 27608136 DOI: 10.1021/acsami.6b08866] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.
Collapse
Affiliation(s)
- Chuan Qian
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Jia Sun
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Ling-An Kong
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Guangyang Gou
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Junliang Yang
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Jun He
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
| | - Yongli Gao
- Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University , Changsha, Hunan 410083, P. R. China
- Department of Physics and Astronomy, University of Rochester , Rochester, New York 14627, United States
| | - Qing Wan
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, P. R. China
| |
Collapse
|
18
|
Wan CJ, Liu YH, Zhu LQ, Feng P, Shi Y, Wan Q. Short-Term Synaptic Plasticity Regulation in Solution-Gated Indium-Gallium-Zinc-Oxide Electric-Double-Layer Transistors. ACS Appl Mater Interfaces 2016; 8:9762-9768. [PMID: 27007748 DOI: 10.1021/acsami.5b12726] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In the biological nervous system, synaptic plasticity regulation is based on the modulation of ionic fluxes, and such regulation was regarded as the fundamental mechanism underlying memory and learning. Inspired by such biological strategies, indium-gallium-zinc-oxide (IGZO) electric-double-layer (EDL) transistors gated by aqueous solutions were proposed for synaptic behavior emulations. Short-term synaptic plasticity, such as paired-pulse facilitation, high-pass filtering, and orientation tuning, was experimentally emulated in these EDL transistors. Most importantly, we found that such short-term synaptic plasticity can be effectively regulated by alcohol (ethyl alcohol) and salt (potassium chloride) additives. Our results suggest that solution gated oxide-based EDL transistors could act as the platforms for short-term synaptic plasticity emulation.
Collapse
Affiliation(s)
- Chang Jin Wan
- School of Electronic Science & Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201, China
| | - Yang Hui Liu
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201, China
| | - Li Qiang Zhu
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201, China
| | - Ping Feng
- School of Electronic Science & Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Yi Shi
- School of Electronic Science & Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Qing Wan
- School of Electronic Science & Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201, China
| |
Collapse
|
19
|
Abstract
Low-temperature sol-gel processed silica electrolyte films showed a high specific capacitance of 3.0 μF/cm(2) due to the electric-double-layer (EDL) effect. Oxide-based transistors gated by such silica electrolyte films show a high on/off ratio (>10(7)) and a very low operation voltage (<2.0 V). The proton-related dynamic modulation in these devices makes them ideal candidates for biological synapse emulation. Short-term synaptic plasticity, such as paired pulse facilitation, was successfully emulated. Most importantly, spiking and logic operation were also demonstrated when two lateral in-plane gates were used as the presynaptic inputs. Our oxide-based EDL transistors gated by sol-gel processed silica electrolyte films provide an interesting approach for synaptic behavior emulation, which is interesting for brain-inspired neuromorphic systems.
Collapse
Affiliation(s)
- Feng Shao
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Yi Yang
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Li Qiang Zhu
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Ping Feng
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| | - Qing Wan
- School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093, China
| |
Collapse
|
20
|
Abstract
Neuromorphic systems are used in variety of circumstances: as parts of sensory systems, for modeling parts of neural systems and for analog signal processing. In the sensory processing domain, neuromorphic systems can be considered in three parts: pre-transduction processing, transduction itself, and post-transduction processing. Neuromorphic systems include transducers for light, odors, and touch but so far neuromorphic applications in the sound domain have used standard microphones for transduction. We discuss why this is the case and describe what research has been done on neuromorphic approaches to transduction. We make a case for a change of direction toward systems where sound transduction itself has a neuromorphic component.
Collapse
Affiliation(s)
- Leslie S Smith
- Computing Science and Mathematics, University of Stirling Stirling, UK
| |
Collapse
|
21
|
Abstract
Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2−x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.
Collapse
Affiliation(s)
- Marina Ignatov
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany
| | - Martin Ziegler
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany
| | - Mirko Hansen
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany
| | - Adrian Petraru
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany
| | - Hermann Kohlstedt
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel Kiel, Germany
| |
Collapse
|
22
|
Thomas A, Niehörster S, Fabretti S, Shepheard N, Kuschel O, Küpper K, Wollschläger J, Krzysteczko P, Chicca E. Tunnel junction based memristors as artificial synapses. Front Neurosci 2015; 9:241. [PMID: 26217173 PMCID: PMC4493388 DOI: 10.3389/fnins.2015.00241] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 06/24/2015] [Indexed: 11/30/2022] Open
Abstract
We prepared magnesia, tantalum oxide, and barium titanate based tunnel junction structures and investigated their memristive properties. The low amplitudes of the resistance change in these types of junctions are the major obstacle for their use. Here, we increased the amplitude of the resistance change from 10% up to 100%. Utilizing the memristive properties, we looked into the use of the junction structures as artificial synapses. We observed analogs of long-term potentiation, long-term depression and spike-time dependent plasticity in these simple two terminal devices. Finally, we suggest a possible pathway of these devices toward their integration in neuromorphic systems for storing analog synaptic weights and supporting the implementation of biologically plausible learning mechanisms.
Collapse
Affiliation(s)
- Andy Thomas
- Thin Films and Physics of Nanostructures, Bielefeld UniversityBielefeld, Germany
- IFW Dresden, Institute for Metallic MaterialsDresden, Germany
| | - Stefan Niehörster
- Thin Films and Physics of Nanostructures, Bielefeld UniversityBielefeld, Germany
| | - Savio Fabretti
- Thin Films and Physics of Nanostructures, Bielefeld UniversityBielefeld, Germany
| | - Norman Shepheard
- Thin Films and Physics of Nanostructures, Bielefeld UniversityBielefeld, Germany
- Cognitive Interaction Technology Center of Excellence and Faculty of Technology, Bielefeld UniversityBielefeld, Germany
| | - Olga Kuschel
- Fachbereich Physik and Center of Physics and Chemistry of New Materials, Osnabrück UniversityOsnabrück, Germany
| | - Karsten Küpper
- Fachbereich Physik and Center of Physics and Chemistry of New Materials, Osnabrück UniversityOsnabrück, Germany
| | - Joachim Wollschläger
- Fachbereich Physik and Center of Physics and Chemistry of New Materials, Osnabrück UniversityOsnabrück, Germany
| | - Patryk Krzysteczko
- Thin Films and Physics of Nanostructures, Bielefeld UniversityBielefeld, Germany
- Physikalisch Technische BundesanstaltBraunschweig, Germany
| | - Elisabetta Chicca
- Cognitive Interaction Technology Center of Excellence and Faculty of Technology, Bielefeld UniversityBielefeld, Germany
| |
Collapse
|
23
|
Stefanini F, Neftci EO, Sheik S, Indiveri G. PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems. Front Neuroinform 2014; 8:73. [PMID: 25232314 PMCID: PMC4152885 DOI: 10.3389/fninf.2014.00073] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 08/01/2014] [Indexed: 11/13/2022] Open
Abstract
Neuromorphic hardware offers an electronic substrate for the realization of asynchronous event-based sensory-motor systems and large-scale spiking neural network architectures. In order to characterize these systems, configure them, and carry out modeling experiments, it is often necessary to interface them to workstations. The software used for this purpose typically consists of a large monolithic block of code which is highly specific to the hardware setup used. While this approach can lead to highly integrated hardware/software systems, it hampers the development of modular and reconfigurable infrastructures thus preventing a rapid evolution of such systems. To alleviate this problem, we propose PyNCS, an open-source front-end for the definition of neural network models that is interfaced to the hardware through a set of Python Application Programming Interfaces (APIs). The design of PyNCS promotes modularity, portability and expandability and separates implementation from hardware description. The high-level front-end that comes with PyNCS includes tools to define neural network models as well as to create, monitor and analyze spiking data. Here we report the design philosophy behind the PyNCS framework and describe its implementation. We demonstrate its functionality with two representative case studies, one using an event-based neuromorphic vision sensor, and one using a set of multi-neuron devices for carrying out a cognitive decision-making task involving state-dependent computation. PyNCS, already applicable to a wide range of existing spike-based neuromorphic setups, will accelerate the development of hybrid software/hardware neuromorphic systems, thanks to its code flexibility. The code is open-source and available online at https://github.com/inincs/pyNCS.
Collapse
Affiliation(s)
- Fabio Stefanini
- Department of Information Technology and Electrical Engineering, Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Emre O Neftci
- Department of Bioengineering, Institute for Neural Computation, University of California at San Diego La Jolla, CA, USA
| | - Sadique Sheik
- Department of Information Technology and Electrical Engineering, Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Giacomo Indiveri
- Department of Information Technology and Electrical Engineering, Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland
| |
Collapse
|
24
|
Ponulak F, Hopfield JJ. Rapid, parallel path planning by propagating wavefronts of spiking neural activity. Front Comput Neurosci 2013; 7:98. [PMID: 23882213 PMCID: PMC3714542 DOI: 10.3389/fncom.2013.00098] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2013] [Accepted: 06/26/2013] [Indexed: 12/04/2022] Open
Abstract
Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware.
Collapse
Affiliation(s)
- Filip Ponulak
- Brain Corporation San Diego, CA, USA ; Department of Molecular Biology, Princeton University Princeton, NJ, USA
| | | |
Collapse
|