The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the pa...The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.展开更多
The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to th...The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to the limitation of reconfiguration conditions,there are inconsistencies in the turn-on voltage and operating current before and after the reconfiguration of neuromorphic devices,which leads to huge difficulties in hardware application development and is an urgent problem to be solved.In this work,we introduced light as a regulatory means in the memristor and achieved the reconfiguration of volatile(endurance~10^(6) cycles)and non-volatile(retention~10^(4 )s)characteristics with a unified working parameter through the photoelectric coupling mode.The switching voltage of the device can be controlled 100%by this method without any limiting current.This will allow neurons and synapses to be dynamically allocated on demand.We completed the verification such as Morse code decoding,Poisson coded image recognition,denoising in the image recognition process,and intelligent traffic signal recognition hardware system under different work modes.It is verified that the device can dynamically adjust the neuromorphic according to needs,providing a new idea for the further integration of neuromorphic computing in the future.展开更多
This paper presents a basic block for building large-scale single-electron neural networks. This macro block is completely composed of SET inverter circuits. We present and discuss the basic parts of this device. The ...This paper presents a basic block for building large-scale single-electron neural networks. This macro block is completely composed of SET inverter circuits. We present and discuss the basic parts of this device. The full design and simulation results were done using MATLAB and SIMON, which are a single-electron tunnel device and circuit simulator based on a Monte Carlo method. Special measures had to be taken in order to simulate this circuit correctly in SIMON and compare results with those of SPICE simulation done before. Moreover, we study part of the network as a memory cell with the idea of combining the extremely low-power properties of the SET and the compact design.展开更多
Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challeng...Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.展开更多
Functional synaptogenesis and network emergence are signature endpoints of neurogenesis. These behaviors provide higher-order confirmation that biochemical and cellular processes necessary for neurotransmitter release...Functional synaptogenesis and network emergence are signature endpoints of neurogenesis. These behaviors provide higher-order confirmation that biochemical and cellular processes necessary for neurotransmitter release, post-synaptic detection and network propagationof neuronal activity have been properly expressed and coordinated among cells. The development of synaptic neurotransmission can therefore be considered a defining property of neurons. Although dissociated primary neuron cultures readily form functioning synapses and network behaviors in vitro, continuously cultured neurogenic cell lines have historically failed to meet these criteria. Therefore, in vitro-derived neuron models that develop synaptic transmission are critically needed for a wide array of studies, including molecular neuroscience, developmental neurogenesis, disease research and neurotoxicology. Over the last decade, neurons derived from various stem cell lines have shown varying ability to develop into functionally mature neurons. In this review, we will discuss the neurogenic potential of various stem cells populations, addressing strengths and weaknesses of each, with particular attention to the emergence of functional behaviors. We will propose methods to functionally characterize new stem cell-derived neuron(SCN) platforms to improve their reliability as physiological relevant models. Finally, we will review how synaptically active SCNs can be applied to accelerate research in a variety of areas. Ultimately, emphasizing the critical importance of synaptic activity and network responses as a marker of neuronal maturation is anticipated to result in in vitro findings that better translate to efficacious clinical treatments.展开更多
The brain’s selective visual attention mechanism(SVAM)enables robust visual recognition in noisy environment through diverse neural action potential peaks acting as filters.Spiking neural networks(SNNs)mimic this par...The brain’s selective visual attention mechanism(SVAM)enables robust visual recognition in noisy environment through diverse neural action potential peaks acting as filters.Spiking neural networks(SNNs)mimic this paradigm but limited noise immunity and high write current density hinder brain-like efficiency.Hardware implementing SVAM necessitates spiking spintronic devices with noise-resistant and low operation current densities;such devices remain unreported.Here,we report an orbit-torque(OT)actuated ferromagnetic spiking synapse and neuron featuring a tunable peak action potential.These are more akin to the biological neurons with varying sensitivities to external sensory stimuli,thereby augmenting the perception aptitude of the system in complex surroundings.Capitalizing on the high-efficient OT,the ferromagnetic device demands a write current density of 5×10^(6) A/cm^(2),which is an order of magnitude lower than other spiking devices actuated by spin-orbit torque.Leveraging these neuromorphic devices,an all-spin SNN with low current density and tunable action potential peak has been fabricated,successfully mimicking the SVAM.In complex noise environment,the SNN achieves 92%on Cifar-10 and 95%on MNIST dataset,surpassing state-of-the-art spin-based SNNs by 5%.Our work provides a promising avenue for exploring the SVAM-inspired spiking neuromorphic devices,enhancing the bionic performance of the SNNs.展开更多
The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)ha...The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)has been proposed for the efficient processing of unstructured data,bypassing the von Neumann bottleneck of current computing architecture.The development of MNC would provide massively parallel processing of unstructured data,realizing the cognitive Al in edge and wearable systems.In this review,recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories,volatile switches,synaptic plasticity,neuronal models,and memristive neural network.展开更多
基金Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province(Grant No.2023GGJS142)the Key Scientific Research of Colleges and Universities in Henan Province,China(Grant No.25A120009)+1 种基金Changzhou Leading Innovative Talent Introduction and Cultivation Project(Grant No.CQ20240102)Changzhou Applied Basic Research Program(Grant No.CJ20253065)。
文摘The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.
基金supported by Science and Technology Project of Hebei Education Department(grant no.QN2023092)High-level Talent Research Startup Project of Hebei University(grant no.521100221071,521000981426,521100223225)+19 种基金National Key R&D Plan"Nano Frontier"Key Special Project(Grant Nos.2024YFA1208400,2021YFA1200502)National Key R&D Program Disruptive Technology Innovation Project(Grant No.2024YFF1504300)National Natural Science Foundation of China(Grant Nos.62004056,62104058,Grant No.61874158)National Major R&D Project Cultivation Projects(Grant No.92164109)Natural Science Foundation of Hebei Province(Grant Nos.F2021201045,F2021201022,F2022201054,F2023201044,F2022201002)Special Support Funds for National High-Level Talents(Grant No.041500120001)Hebei Province Yanzhao Young Scientist Project(Grant No.F2023201076)Support Program for the Top Young Talents of Hebei Province(Grant No.70280011807)Hebei Province High-Level Talent Funding Project(Grant No.B20231003)Strategic Leading Science and Technology Special Project of Chinese Academy of Sciences(Grant No.XDB44000000-7)Interdisciplinary Research Program of Natural Science of Hebei University(Grant No.DXK202101)Institute of Life Sciences and Green Development(Grant No.521100311)Outstanding Young Scientific Research and Innovation Team of Hebei University(Grant No.605020521001)Advanced Talents Incubation Program of Hebei University(Grant Nos.521000981426,521100221071,521100224232,521000981363)Science and Technology Project of Hebei Education Department(Grant Nos.QN2020178,QN2021026)Baoding Science and Technology Plan Project(Grant No.2172P011)Hebei Province Key R&D Plan Projects(Grant No.22311101D)Baoding Science and Technology Plan Project(Grant No.2272P014)Regional Innovation and Development Joint Fund Key Project(Grant No.U23A20365)Hebei Province Natural Science Foundation(Grant No.F2023201044).
文摘The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to the limitation of reconfiguration conditions,there are inconsistencies in the turn-on voltage and operating current before and after the reconfiguration of neuromorphic devices,which leads to huge difficulties in hardware application development and is an urgent problem to be solved.In this work,we introduced light as a regulatory means in the memristor and achieved the reconfiguration of volatile(endurance~10^(6) cycles)and non-volatile(retention~10^(4 )s)characteristics with a unified working parameter through the photoelectric coupling mode.The switching voltage of the device can be controlled 100%by this method without any limiting current.This will allow neurons and synapses to be dynamically allocated on demand.We completed the verification such as Morse code decoding,Poisson coded image recognition,denoising in the image recognition process,and intelligent traffic signal recognition hardware system under different work modes.It is verified that the device can dynamically adjust the neuromorphic according to needs,providing a new idea for the further integration of neuromorphic computing in the future.
文摘This paper presents a basic block for building large-scale single-electron neural networks. This macro block is completely composed of SET inverter circuits. We present and discuss the basic parts of this device. The full design and simulation results were done using MATLAB and SIMON, which are a single-electron tunnel device and circuit simulator based on a Monte Carlo method. Special measures had to be taken in order to simulate this circuit correctly in SIMON and compare results with those of SPICE simulation done before. Moreover, we study part of the network as a memory cell with the idea of combining the extremely low-power properties of the SET and the compact design.
基金The authors would like to thank the National Natural Science Foundation of China(Nos.92163210,U1932125,52172143)Science and Technology Program of Guangzhou(No.2019050001)Natural Science Foundation of Guangdong Province(No.2020A1515010996).
文摘Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.
文摘Functional synaptogenesis and network emergence are signature endpoints of neurogenesis. These behaviors provide higher-order confirmation that biochemical and cellular processes necessary for neurotransmitter release, post-synaptic detection and network propagationof neuronal activity have been properly expressed and coordinated among cells. The development of synaptic neurotransmission can therefore be considered a defining property of neurons. Although dissociated primary neuron cultures readily form functioning synapses and network behaviors in vitro, continuously cultured neurogenic cell lines have historically failed to meet these criteria. Therefore, in vitro-derived neuron models that develop synaptic transmission are critically needed for a wide array of studies, including molecular neuroscience, developmental neurogenesis, disease research and neurotoxicology. Over the last decade, neurons derived from various stem cell lines have shown varying ability to develop into functionally mature neurons. In this review, we will discuss the neurogenic potential of various stem cells populations, addressing strengths and weaknesses of each, with particular attention to the emergence of functional behaviors. We will propose methods to functionally characterize new stem cell-derived neuron(SCN) platforms to improve their reliability as physiological relevant models. Finally, we will review how synaptically active SCNs can be applied to accelerate research in a variety of areas. Ultimately, emphasizing the critical importance of synaptic activity and network responses as a marker of neuronal maturation is anticipated to result in in vitro findings that better translate to efficacious clinical treatments.
基金supported by the National Natural Science Foundation of China(12304160,12304161,62172155,U22A2027,62274183,and 62301595)the Research Foundation from National University of Defense Technology(ZK24-18,23-ZZCX-ZZGC-01-02,and 22-ZZCX-046-02)。
文摘The brain’s selective visual attention mechanism(SVAM)enables robust visual recognition in noisy environment through diverse neural action potential peaks acting as filters.Spiking neural networks(SNNs)mimic this paradigm but limited noise immunity and high write current density hinder brain-like efficiency.Hardware implementing SVAM necessitates spiking spintronic devices with noise-resistant and low operation current densities;such devices remain unreported.Here,we report an orbit-torque(OT)actuated ferromagnetic spiking synapse and neuron featuring a tunable peak action potential.These are more akin to the biological neurons with varying sensitivities to external sensory stimuli,thereby augmenting the perception aptitude of the system in complex surroundings.Capitalizing on the high-efficient OT,the ferromagnetic device demands a write current density of 5×10^(6) A/cm^(2),which is an order of magnitude lower than other spiking devices actuated by spin-orbit torque.Leveraging these neuromorphic devices,an all-spin SNN with low current density and tunable action potential peak has been fabricated,successfully mimicking the SVAM.In complex noise environment,the SNN achieves 92%on Cifar-10 and 95%on MNIST dataset,surpassing state-of-the-art spin-based SNNs by 5%.Our work provides a promising avenue for exploring the SVAM-inspired spiking neuromorphic devices,enhancing the bionic performance of the SNNs.
基金supported by Samsung Electronics Co.,Ltd(No.10201214-08153-01)supported by Convergent Technology R&D Program for Human Augmentation through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(No.NRF-2020M3C1B8081519)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIP)(No.NRF-2020M3F3A2A02082445).
文摘The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)has been proposed for the efficient processing of unstructured data,bypassing the von Neumann bottleneck of current computing architecture.The development of MNC would provide massively parallel processing of unstructured data,realizing the cognitive Al in edge and wearable systems.In this review,recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories,volatile switches,synaptic plasticity,neuronal models,and memristive neural network.
