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Daddinounou S, Vatajelu EI. Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN. Front Neurosci 2024; 18:1387339. [PMID: 38817912 PMCID: PMC11137280 DOI: 10.3389/fnins.2024.1387339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/22/2024] [Indexed: 06/01/2024] Open
Abstract
In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.
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Milano G, Raffone F, Bejtka K, De Carlo I, Fretto M, Pirri FC, Cicero G, Ricciardi C, Valov I. Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires. NANOSCALE HORIZONS 2024; 9:416-426. [PMID: 38224292 DOI: 10.1039/d3nh00476g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Memristive devices have been demonstrated to exhibit quantum conductance effects at room temperature. In these devices, a detailed understanding of the relationship between electrochemical processes and ionic dynamic underlying the formation of atomic-sized conductive filaments and corresponding electronic transport properties in the quantum regime still represents a challenge. In this work, we report on quantum conductance effects in single memristive Ag nanowires (NWs) through a combined experimental and simulation approach that combines advanced classical molecular dynamics (MD) algorithms and quantum transport simulations (DFT). This approach provides new insights on quantum conductance effects in memristive devices by unravelling the intrinsic relationship between electronic transport and atomic dynamic reconfiguration of the nanofilment, by shedding light on deviations from integer multiples of the fundamental quantum of conductance depending on peculiar dynamic trajectories of nanofilament reconfiguration and on conductance fluctuations relying on atomic rearrangement due to thermal fluctuations.
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Affiliation(s)
- Gianluca Milano
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
| | - Federico Raffone
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Katarzyna Bejtka
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
- Centre for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy
| | - Ivan De Carlo
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
- Department of Electronics and Telecommunications, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Matteo Fretto
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
| | - Fabrizio Candido Pirri
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
- Centre for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy
| | - Giancarlo Cicero
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Carlo Ricciardi
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Ilia Valov
- Forschungszentrum Jülich, Institute of Electrochemistry and Energy System, WilhelmJohnen-Straße, 52428, Jülich, Germany
- "Acad. Evgeni Budevski" (IEE-BAS), Bulgarian Academy of Sciences (BAS), Acad. G. Bonchev Str., Block 10, 1113 Sofia, Bulgaria
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Milano G, Cultrera A, Boarino L, Callegaro L, Ricciardi C. Tomography of memory engrams in self-organizing nanowire connectomes. Nat Commun 2023; 14:5723. [PMID: 37758693 PMCID: PMC10533552 DOI: 10.1038/s41467-023-40939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/11/2023] [Indexed: 09/29/2023] Open
Abstract
Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite having been shown that the emergent behavior relies on weight plasticity at single junction/synapse level and on wiring plasticity involving topological changes, a shift to multiterminal paradigms is needed to unveil dynamics at the network level. Here, we report on tomographical evidence of memory engrams (or memory traces) in nanowire connectomes, i.e., physicochemical changes in biological neural substrates supposed to endow the representation of experience stored in the brain. An experimental/modeling approach shows that spatially correlated short-term plasticity effects can turn into long-lasting engram memory patterns inherently related to network topology inhomogeneities. The ability to exploit both encoding and consolidation of information on the same physical substrate would open radically new perspectives for in materia computing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge.
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Affiliation(s)
- Gianluca Milano
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy.
| | - Alessandro Cultrera
- Quantum Metrology and Nanotechnologies Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Luca Boarino
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Luca Callegaro
- Quantum Metrology and Nanotechnologies Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Carlo Ricciardi
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Torino, Italy.
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Paroli B, Martini G, Potenza MAC, Siano M, Mirigliano M, Milani P. Solving classification tasks by a receptron based on nonlinear optical speckle fields. Neural Netw 2023; 166:634-644. [PMID: 37604074 DOI: 10.1016/j.neunet.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 06/07/2023] [Accepted: 08/02/2023] [Indexed: 08/23/2023]
Abstract
Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of non-linear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, and non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.
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Affiliation(s)
- B Paroli
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
| | - G Martini
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
| | - M A C Potenza
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
| | - M Siano
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
| | - M Mirigliano
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
| | - P Milani
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via G. Celoria 16, 20133, Milan, Italy.
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Han Z, Chen N, Wei X, Yuan M, Li H. Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1241. [PMID: 37628273 PMCID: PMC10453749 DOI: 10.3390/e25081241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov-Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.
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Affiliation(s)
- Zhen Han
- School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
| | - Naipeng Chen
- International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xiaofeng Wei
- The 20th Research Institute of China Electronics Technology Group Corporation, Xi’an 710068, China
| | - Manman Yuan
- School of Computer Science, Inner Mongolia University, Hohhot 010021, China
| | - Huijia Li
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
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