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Effect of applied electric fields on supralinear dendritic integration of interneuron

Chinese Physics B 2024年第2期33卷 84-95页

作者：樊亚琴魏熙乐卢梅丽伊国胜 Tianjin Key Laboratory of Process measurement and control School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China school of Information technology Engineering Tianjin University of Technology and EducationTianjin 300222China

Evidences show that electric fields(EFs)induced by the magnetic stimulation could modulates brain activities by regulating the excitability of GABAergic interneuron.However,it is still unclear how and why the EF-induced polarization affects the interneuron response as the interneuron receives NMDA synaptic inputs.Considering the key role of NMDA receptor-mediated supralinear dendritic integration in neuronal computations,we suppose that the applied EFs could functionally modulate interneurons’response via regulating dendritic integration.At first,we build a simplified multi-dendritic circuit model with inhomogeneous extracellular potentials,which characterizes the relationship among EF-induced spatial polarizations,dendritic integration,and somatic output.By performing model-based singular perturbation analysis,it is found that the equilibrium point of fast subsystem can be used to asymptotically depict the subthreshold input–output(sI/O)relationship of dendritic integration.It predicted that EF-induced strong depolarizations on the distal dendrites reduce the dendritic saturation output by reducing driving force of synaptic input,and it shifts the steep change of sI/O curve left by reducing stimulation threshold of triggering NMDA spike.Also,the EF modulation prefers the global dendritic integration with asymmetric scatter distribution of NMDA synapses.Furthermore,we identify the respective contribution of EF-regulated dendritic integration and EF-induced somatic polarization to an action potential generation and find that they have an antagonistic effect on AP generation due to the varied NMDA spike threshold under EF stimulation.

关键词： GABAergic interneuron electrical field supralinear dendritic integration action potential generation

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Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

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Chinese Physics B 2024年第6期33卷 614-626页

作者：魏熙乐任泽宇卢梅丽樊亚琴常思远 The Tianjin Key Laboratory of Process measurement and control School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China school of Information technology Engineering Tianjin University of Technology and EducationTianjin 300074China

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

关键词： hippocampal network epileptiform dendritic oscillation traveling wave electric field coupling

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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Science China Technological Sciences 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan school of Microelectronics and control Engineering Changzhou University school of Computer Science and technology Harbin Institute of Technology

Inspired by basic circuit connection methods, memristors can also be utilized in the construction of complex discrete chaotic systems. To investigate the dynamical effects of hybrid memristors, we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations, respectively. Taking the HTMH mapping structure with hybrid parallel/cascade operation as an example, this map possesses a spatial invariant set whose stability is closely related to the initial states of the memristors. Dynamics distributions and bifurcation behaviours dependent on the control parameters are explored with numerical tools. Specifically, the memristor initial offset-boosting mechanism is theoretically demonstrated, and memristor initial offset-boosting behaviours are numerically verified. The results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite attractors. In addition, an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high randomness. Notably, the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs) to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

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Defence technology（防务技术） 2024年第2期32卷 104-119页

作者： Fei Zhang Xingling Shao Yi Xia Wendong Zhang National Key Laboratory for Electronic measurement technology School of Instrument and ElectronicsNorth University of ChinaTaiyuan030051China

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

关键词： Elliptical encirclement Reinforced performances Wind perturbations UAVs

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Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

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Chinese Physics B 2024年第4期33卷 275-283页

作者：程涛赵润盛王爽王睿马鸿洋 school of Sciences Qingdao University of TechnologyQingdao 266033China school of Information and control Engineering Qingdao University of TechnologyQingdao 266033China

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

关键词： parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network

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Design of a novel hybrid quantum deep neural network in INEQR images classification

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Chinese Physics B 2024年第6期33卷 230-238页

作者：王爽王柯涵程涛赵润盛马鸿洋郭帅 school of Information and control Engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

关键词： quantum computing image classification quantum–classical hybrid neural network quantum image representation interpolation

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Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

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Chinese Physics B 2024年第4期33卷 285-295页

作者：赵润盛马鸿洋程涛王爽范兴奎 school of Sciences Qingdao University of TechnologyQingdao 266033China school of Information and control Engineering Qingdao University of TechnologyQingdao 266033China

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

关键词： readout errors quantum generative adversarial networks bit-flip averaging method fault tolerant mechanisms

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Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

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Chinese Physics B 2024年第6期33卷 262-270页

作者：陈庆辉姬宇欣王柯涵马鸿洋纪乃华 school of Information and control Engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

关键词： quantum error correction topological quantum stabilizer code reinforcement learning attention mechanism

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Recurrent neural network decoding of rotated surface codes based on distributed strategy

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Chinese Physics B 2024年第4期33卷 322-330页

作者：李帆李熬庆甘启迪马鸿洋 school of Information and control Engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

关键词： quantum error correction rotated surface code recurrent neural network distributed strategy

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Toroidal dipole response in rectangular waveguide:used to generate vector beams and vector vortex beams

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Chinese Optics Letters 2024年第1期22卷 90-97页

作者：罗灏陈聪高鹏冯越任紫燕乔昱嘉刘海 school of Information and control Engineering China University of Mining and TechnologyXuzhou 221116China

Toroidal multipole is a special current distribution that has many different characteristics from electric multipole and magnetic multipole distributions.Because of its special properties,the toroidal dipole is a research hotspot in the field of metamaterials and nanophotonics.However,the low scattering of the toroidal dipole moment makes its excitation a challenging task.At present,there are relatively few studies on its specific engineering applications.In this paper,by slotting in the rectangular cavity,the excitation of an equivalent toroidal dipole is successfully achieved over a wide frequency range of 53-58 GHz.Results indicate that under the action of the toroidal dipole,the TE_(10)mode electromagnetic waves transmitted in the rectangular waveguide are converted into vector beams and are radiated outwards.Further adjusting the spatial distribution of the magnetic dipoles in the toroidal dipoles yields results that indicate that the resonance mode in the slot is still dominated by the magnetic toroidal dipole moment,and the electromagnetic waves radiating outward are vortex beams carrying vector polarization.The scattered energy of each dipole moment inside the antenna is calculated.This calculation verifies that the mass of the vector beam and vector vortex beam is closely related to the toroidal dipole supported by this antenna.The proposed structure can be applied to explorations in vortex filtering,in photon entanglement,and in the photonic spin Hall effect.

关键词： toroidal dipole vector beams vector vortex beams

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