The Experts below are selected from a list of 14223 Experts worldwide ranked by ideXlab platform
Xue Liang - One of the best experts on this subject based on the ideXlab platform.
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a Spiking Neuron constructed by the skyrmion based spin torque nano oscillator
Applied Physics Letters, 2020Co-Authors: Xue Liang, Xichao Zhang, Jing Xia, Motohiko Ezawa, Yuelei Zhao, Guoping Zhao, Yan ZhouAbstract:Magnetic skyrmions are particle-like topological spin configurations, which can carry binary information and thus are promising building blocks for future spintronic devices. In this work, we investigate the relationship between the skyrmion dynamics and the characteristics of injected current in a skyrmion-based spin torque nano-oscillator, where the excitation source is introduced from a point nano-contact at the center of the nanodisk. It is found that the skyrmion will move away from the center of the nanodisk if it is driven by a spin-polarized current; however, it will return to the initial position in the absence of stimulus. Therefore, we propose a skyrmion-based artificial Spiking Neuron, which can effectively implement the leaky-integrate-fire operation. We study the feasibility of the skyrmion-based Spiking Neuron by using micromagnetic simulations. Our results may provide useful guidelines for building future magnetic neural networks with ultra-high density and ultra-low energy consumption.
Yan Zhou - One of the best experts on this subject based on the ideXlab platform.
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a Spiking Neuron constructed by the skyrmion based spin torque nano oscillator
Applied Physics Letters, 2020Co-Authors: Xue Liang, Xichao Zhang, Jing Xia, Motohiko Ezawa, Yuelei Zhao, Guoping Zhao, Yan ZhouAbstract:Magnetic skyrmions are particle-like topological spin configurations, which can carry binary information and thus are promising building blocks for future spintronic devices. In this work, we investigate the relationship between the skyrmion dynamics and the characteristics of injected current in a skyrmion-based spin torque nano-oscillator, where the excitation source is introduced from a point nano-contact at the center of the nanodisk. It is found that the skyrmion will move away from the center of the nanodisk if it is driven by a spin-polarized current; however, it will return to the initial position in the absence of stimulus. Therefore, we propose a skyrmion-based artificial Spiking Neuron, which can effectively implement the leaky-integrate-fire operation. We study the feasibility of the skyrmion-based Spiking Neuron by using micromagnetic simulations. Our results may provide useful guidelines for building future magnetic neural networks with ultra-high density and ultra-low energy consumption.
Hiroyuki Torikai - One of the best experts on this subject based on the ideXlab platform.
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a novel pwc Spiking Neuron model Neuron like bifurcation scenarios and responses
IEEE Transactions on Circuits and Systems I-regular Papers, 2012Co-Authors: Y Yamashita, Hiroyuki TorikaiAbstract:A novel electrical circuit Spiking Neuron model that has a piece-wise-constant vector field with a state-dependent reset is presented. It is shown that the model exhibits six kinds of border-collision bifurcations, where their bifurcation sets are derived and are summarized into two parameter diagrams. Then, using the diagrams, systematic synthesis procedures of the presented model so that it can reproduce four kinds of bifurcation scenarios that are typically observed in standard Neuron models are presented. It is shown that the model can reproduce the bifurcation scenarios as well as corresponding nonlinear response characteristics observed in model and biological Neurons. Occurrences of typical Neuron-like bifurcations are confirmed by experimental measurements.
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a generalized rotate and fire digital Spiking Neuron model and its on fpga learning
IEEE Transactions on Circuits and Systems Ii-express Briefs, 2011Co-Authors: Takashi Matsubara, Hiroyuki Torikai, Tetsuya HishikiAbstract:A generalized rotate-and-fire digital Spiking Neuron model that can be implemented by a simple asynchronous sequential logic circuit is proposed. The model can exhibit various nonlinear phenomena and responses to stimulation inputs. It is shown that the model can reproduce five types of inhibitory responses of Izhikevich's simplified ordinary differential equation Neuron model. In addition, field programmable gate array experiments show that a learning algorithm enables the model to automatically reproduce nonlinear responses of a biological Neuron and Neuron models in the Neuron simulator.
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a novel rotate and fire digital Spiking Neuron and its Neuron like bifurcations and responses
IEEE Transactions on Neural Networks, 2011Co-Authors: Tetsuya Hishiki, Hiroyuki TorikaiAbstract:A novel rotate-and-fire digital Spiking Neuron is presented. The digital Neuron is a wired system of shift registers and thus it is suited to on-chip learning unlike many other analog Spiking Neuron models. By adjusting the wiring pattern among the registers, the digital Neuron can generate spike trains with various spike patterns and can exhibit related bifurcations. A discrete-continuous hybrid map, which describes the Neuron dynamics without any approximation, is derived analytically. Using the hybrid map, it is shown that the digital Spiking Neuron can mimic typical bifurcation phenomena and various nonlinear responses of biological Neurons.
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a novel hybrid Spiking Neuron bifurcations responses and on chip learning
IEEE Transactions on Circuits and Systems I-regular Papers, 2010Co-Authors: S Hashimoto, Hiroyuki TorikaiAbstract:We present a novel hybrid Spiking Neuron that is a wired system of shift registers and behaves like an analog Spiking Neuron model. The presented Neuron exhibits various bifurcation phenomena and response characteristics to an input spike train. We derive continuous discrete hybrid maps that can describe the Neuron dynamics analytically. By using these maps, the typical mechanisms of bifurcations and responses are clarified. We also present a novel field-programmable gate-array-friendly online learning algorithm for the Neuron. It is shown that the algorithm enables the Neuron to reconstruct the response characteristics of another Neuron with unknown parameter values. Typical learning functions are also validated by experimental measurements.
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ICONIP (1) - Theoretical analysis of various synchronizations in pulse-coupled digital Spiking Neurons
Neural Information Processing. Theory and Algorithms, 2010Co-Authors: Hirofumi Ijichi, Hiroyuki TorikaiAbstract:A digital Spiking Neuron is a wired system of shift registers that can mimic nonlinear dynamics of simplified Spiking Neuron models. In this paper, we present a novel pulse-coupled system of the digital Spiking Neurons. The coupled Neurons can exhibit various pseudoperiodic synchronizations, non-periodic synchronizations, and related bifurcations. We derive theoretical parameter conditions that guarantee occurrence of typical synchronization phenomena. Also, the theoretical results are validated by numerical simulations.
Tara Julia Hamilton - One of the best experts on this subject based on the ideXlab platform.
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racing to learn statistical inference and learning in a single Spiking Neuron with adaptive kernels
Frontiers in Neuroscience, 2014Co-Authors: Saeed Afshar, Libin George, Jonathan Tapson, Andre Van Schaik, Tara Julia HamiltonAbstract:This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple Spiking Neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed Neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single Neuron scale. The rule-set defining the Neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological Neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale Neurons are locked in a race with each other with the fastest Neuron to spike effectively ‘hiding’ its learnt pattern from its neighbors. This use of time as a parameter is central and means that a SKAN network utilizes a minimal connectivity that scales linearly with the number of Neurons. The robustness to noise, low connectivity requirements, high speed and simple building blocks not only make SKAN an interesting Neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA).
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racing to learn statistical inference and learning in a single Spiking Neuron with adaptive kernels
arXiv: Neural and Evolutionary Computing, 2014Co-Authors: Saeed Afshar, Libin George, Jonathan Tapson, Andre Van Schaik, Tara Julia HamiltonAbstract:This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple Spiking Neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed Neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single Neuron scale. The rule-set defining the Neuron is simple there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological Neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale Neurons are locked in a race with each other with the fastest Neuron to spike effectively hiding its learnt pattern from its neighbors. The robustness to noise, high speed and simple building blocks not only make SKAN an interesting Neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA).
A V Emelyanov - One of the best experts on this subject based on the ideXlab platform.
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self adaptive stdp based learning of a Spiking Neuron with nanocomposite memristive weights
Nanotechnology, 2020Co-Authors: Roman Rybka, A V Emelyanov, K E Nikiruy, Alexey Serenko, A V Sitnikov, Yu M Presnyakov, Alexander Sboev, V V RylkovAbstract:Neuromorphic systems consisting of artificial Neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for Spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning Spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a Spiking Neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20) х (LiNbO3-y )100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable Spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.