电磁场耦合忆阻Izhikevich神经网络电活动研究

考虑到电磁场的影响,在Izhikevich神经元模型中引入电场变量和磁通变量,利用电突触耦合构建神经网络,研究电磁场耦合忆阻Izhikevich神经网络集体动力学行为。数值仿真发现：随着电突触耦合强度的增大,神经网络逐渐达到同步状态,并且神经元的放电模式也会随之改变。增大磁场耦合值可以提高神经元的放电活性,并且对网络同步也有一定的促进作用,而增大电场则会抑制神经元的放电活动。另外,当电突触与磁场耦合共同作用时,磁场耦合值越小,电突触耦合更能有效促进网络同步;在电突触耦合强度的作用下,电场抑制电活动的效果更明显。研究结果可望为理解神经系统中的信号编码和传递提供新的见解。     

忆阻Rulkov神经网络同步研究

研究电突触、化学突触以及两者共存对忆阻Rulkov神经模型集体动力学行为的影响。对于两个忆阻Rulkov神经元系统,各种耦合方式都能使系统实现同步。对于不同的耦合强度,神经元呈现不同的放电模式,如方波,三角波,脉冲放电等。当电突触、化学突触同时存在时,系统的同步更依赖于电耦合强度。对全局耦合忆阻Rulkov神经网络同步的研究表明：化学突触单独作用时,同步发生在耦合参数的某个区域范围,当化学耦合强度超过某一阈值时,同步会随着耦合强度的增加而被破坏。电突触单独作用时,系统很快到达同步状态,并且电耦合强度是决定神经元处于静止还是峰放电的关键因素,随着电耦合强度增加,神经元放电频率、振幅增大。当电、化学耦合同时存在时,耦合强度的增加使神经元由静息转变为圆弧放电,并进入同步状态。本文提供了一种通过调整耦合方式和耦合强度,控制神经网络放电模式及其同步的可能方法。     

铝基薄膜忆阻器作为感觉神经系统的习惯化特性

感觉神经系统可在外界刺激与生物体反应之间建立联系.感觉神经系统中的最小单位神经元可直接将外界刺激传递至中枢神经,再由中枢神经通过控制和调节生物体对外界刺激作出反应.神经突触连接了相邻神经元进行脉冲信息传递功能.习惯化是神经突触在信息传递中过滤外界无关信息时的一个基本特性,可以让感觉神经系统更快速地适应外界环境变化.忆阻器模拟神经突触功能在近年获得进展,然而针对以忆阻器为基础的具有习惯化特性的神经突触以及完整神经系统的研究相对匮乏.本文利用磁控溅射技术制备了厚度约为40 nm且含铝纳米颗粒的氮化铝薄膜忆阻器,并发现这种结构忆阻器对于重复的外界刺激有明显的习惯化行为,该行为与感觉神经系统的习惯化特性极为相似.若将这种具有习惯化的神经突触与感觉神经元串联,可形成LIF(leaky integrate-and-fire)生物模型模拟完整的神经系统行为,也为忆阻器在第三代神经网络(脉冲神经网络)中的应用提供理论参考.     

基于忆阻器的脉冲神经网络硬件加速器架构设计

脉冲神经网络(spiking neural network, SNN)作为第三代神经网络,其计算效率更高、资源开销更少,且仿生能力更强,展示出了对于语音、图像处理的优秀潜能.传统的脉冲神经网络硬件加速器通常使用加法器模拟神经元对突触权重的累加.这种设计对于硬件资源消耗较大、神经元/突触集成度不高、加速效果一般.因此,本工作开展了对拥有更高集成度、更高计算效率的脉冲神经网络推理加速器的研究.阻变式存储器(resistive random access memory, RRAM)又称忆阻器(memristor),作为一种新兴的存储技术,其阻值随电压变化而变化,可用于构建crossbar架构模拟矩阵运算,已经在被广泛应用于存算一体(processing in memory, PIM)、神经网络计算等领域.因此,本次工作基于忆阻器阵列,设计了权值存储矩阵,并结合外围电路模拟了LIF(leaky integrate and fire)神经元计算过程.之后,基于LIF神经元模型实现了脉冲神经网络硬件推理加速器设计.该加速器消耗了0.75k忆阻器,集成了24k神经元和192M突触.仿真结果显示,在5...     

忆阻Hopfield神经网络动力学分析及其电路实现

提出一种超多稳态忆阻Hopfield神经网络, 它仅包含3个神经元和一个多稳态忆阻突触。从理论上分析神经网络的耗散性和平衡点的稳定性, 并利用分岔图、李雅普诺夫指数谱和相位图等数值方法分析不同忆阻突触耦合强度对神经网络动力学的影响。网络参数固定时, 揭示与初始状态值密切相关的超多稳态性动力学行为。最后, 设计忆阻Hopfield神经网络的模拟等效电路, 并通过PSIM电路仿真验证MATLAB数值仿真结果。     

忆阻突触耦合环形Hopfield神经网络动力学分析及其电路实现

提出一种新型忆阻器模型, 利用标准非线性理论分析三个忆阻特性, 并设计模拟电路。基于忆阻突触, 构建一个忆阻突触耦合环形Hopfield神经网络模型。采用分岔图、李雅普诺夫指数谱、时序图等方法, 揭示与忆阻突触密切相关的特殊动力学行为。数值仿真表明: 在忆阻突触权重的影响下, 它能够产生多种对称簇发放电模式和复杂的混沌行为。实现了该忆阻环形神经网络的模拟等效电路, 并由PSIM电路仿真验证MATLAB数值仿真的正确性。     

面向神经形态感知和计算的柔性忆阻器基脉冲神经元

受人脑工作模式的启发,脉冲神经元作为人工感知系统和神经形态计算体系的基本计算单元发挥着重要作用.然而,基于传统互补金属氧化物半导体技术的神经元电路结构复杂,功耗高,且缺乏柔韧性,不利于大规模集成和与人体兼容的柔性感知系统的应用.本文制备的柔性忆阻器展示出了稳定的阈值转变特性和优异的机械弯折特性,其弯折半径可达1.5 mm,弯折次数可达10~4次.基于此器件构建的神经元电路实现了神经元的关键积分放电特性,且其频率-输入电压关系具有整流线性单元相似性,可实现基于转换法的脉冲神经网络中神经元的非线性处理功能.此外,基于电子传输机制和构建的核壳模型,对柔性忆阻器的工作机制进行分析,提出了电场和热激发主导的阈值转变机制;进一步对忆阻器和神经元的电学特性进行电路仿真模拟,验证了柔性忆阻器和神经元电路工作机制的合理性.本文对柔性神经元的研究可为神经形态感知和计算系统的构建提供硬件基础和理论指导.     

NbOx忆阻神经元的设计及其在尖峰神经网络中的应用

NbO_x忆阻器凭借其纳米尺寸、阈值切换及局部有源特性在神经形态计算领域展现出巨大的应用前景.对NbO_x忆阻器动力学特性的深入分析和研究有利于忆阻神经元电路的设计和优化.本文基于局部有源理论,采用小信号分析方法对NbO_x忆阻器物理模型展开了研究,定量分析了产生尖峰振荡的区域和条件,并确定了激励信号幅值和尖峰频率之间的定量关系.基于上述理论分析,进一步设计了NbO_x忆阻器神经元,并结合忆阻突触十字交叉阵列,构建了25×10的尖峰神经网络(spiking neuron network,SNN).最后,分别利用频率编码和时间编码两种方式,有效地实现了数字0到9模式的识别功能.     

两类纳米级非线性忆阻器模型及串并联研究

忆阻器是一种新型的非线性动态可变电阻器,其阻值的变化依赖于通过它的电荷量或磁通量.作为第四种基本电路元器件,忆阻器在非易失性存储器、非线性电路及系统、神经形态系统等领域中有巨大的应用潜能.忆阻器串并联组合电路具有比单个忆阻器更为丰富的器件特性,引起了研究者越来越多的关注.本文推导了带有窗函数的闭合形式的电荷及磁通量控制的忆阻器非线性模型,能够有效地模拟忆阻器边缘附近的非线性离子迁移现象,同时保证忆阻器的边界条件.进一步,分别从忆阻器的器件参数和激励阈值两个角度,对忆阻器串并联电路进行了全面的理论推导和数值分析.为了更加直观地观察忆阻器串并联特性,设计了一种基于Matlab的忆阻器串并联图形用户界面,能够清晰地展示两种分类方式下忆阻系统的器件特性,可为忆阻器组合电路的后续研究提供良好的理论参考和实验依据.     

基于串并联磁控忆阻器的耦合行为研究

忆阻器是纳米级器件, 其功耗低, 集成度高, 有着巨大的应用潜能. 单个器件具有丰富的电学性质, 其串并联电路更展现了丰富的动力学行为. 然而, 忆阻器在高密度集成的环境下, 其耦合效应不可忽视. 因此, 本文首先基于磁控忆阻器推导了耦合忆阻器的数学模型. 其次, 在考虑不同极性连接和耦合强度的前提下, 讨论两个磁控忆阻器串并联的耦合情况, 进行了详细的理论分析, 并通过数值仿真探索了耦合效应对忆阻系统的影响. 同时, 设计了基于Matlab的图形用户界面, 直观地展示了不同参数下的耦合特性曲线. 进一步, 本文展示了有无耦合情况下, 初始阻值对忆阻器正常工作范围的影响. 最后, 构建耦合忆阻器的Pspice仿真器, 从电路的角度再次验证了忆阻器间的耦合效应. 实验结果表明: 同极性耦合增强了阻值的改变, 相反极性的耦合减缓了阻值的改变. 这些动力学特性可以很好地应用于忆阻网络中, 也为全面考虑忆阻系统电路的设计提供了强大的理论基础.     

Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network.     

Modeling and dynamics of double Hindmarsh-Rose neuron with memristor-based magnetic coupling and time delay

The firing of a neuron model is mainly affected by the following factors:the magnetic field, external forcing current, time delay, etc. In this paper, a new time-delayed electromagnetic field coupled dual Hindmarsh-Rose neuron network model is constructed. A magnetically controlled threshold memristor is improved to represent the self-connected and the coupled magnetic fields triggered by the dynamic change of neuronal membrane potential for the adjacent neurons. Numerical simulation confirms that the coupled magnetic field can activate resting neurons to generate rich firing patterns, such as spiking firings, bursting firings, and chaotic firings, and enable neurons to generate larger firing amplitudes. The study also found that the strength of magnetic coupling in the neural network also affects the number of peaks in the discharge of bursting firing. Based on the existing medical treatment background of mental illness, the effects of time lag in the coupling process against neuron firing are studied. The results confirm that the neurons can respond well to external stimuli and coupled magnetic field with appropriate time delay, and keep periodic firing under a wide range of external forcing current.     

In- and Anti-transition of Firing Patterns Induced by Random Long-range Connections in Coupled Hindmarsh-Rose Neurons System

研究了在外界刺激电流的作用下,随机的长程关联对耦合的Hindmarsh-Rose神经元放电模式转变的影响．结果表明,当耦合强度较弱时,在神经元网络中加入一定数量的随机的长程关联,神经元的放电模式会从较少的周期态转变到较多的周期态;当耦合强度较强时,在神经元网络中加入一定数量的随机长程关联,神经元的放电模式会产生相反的转变,即从较多的周期态转变到较少的周期态．同时还简单讨论了神经系统的尺度大小和神经元之间的耦合强度,以及不同外界刺激条件下放电模式的强度与临界特性之间的关系．     

Firing activity of complex space-clamped FitzHugh-Nagumo neural networks

We investigate how firing activity of complex neural networks depends on the random long-range connections and coupling strength. Network elements are described by excitable space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength C, there exists a critical fraction of random connections (or randomness) p^*, such that if p > p^* the firing neurons, which are absent in the nearest-neighbor network, occur. The firing activity becomes more frequent as randomness p is further increased. On the other hand, when the p is smaller, there are no active neurons in network, no matter what the value of C is. For a given larger p, there exist optimal coupling strength levels, where firing activity reaches its maximum. To the best of our knowledge, this is a novel mechanism for the emergence of firing activity in neurons.     

Pattern transition and regulation in a subthalamopallidal network under electromagnetic effect

Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied, their influence on Parkinson's disease (PD) has yet to be well explored. By virtue of the magnetic flux variable, this paper studies the transition of firing patterns induced by magnetic induction and the regulation effect of external magnetic radiation on the firing activities of the subthalamopallidal network in basal ganglia. We find: (i) The network reproduces five typical waveforms corresponding to the severity of symptoms: weak cluster, episodic, continuous cluster, episodic, and continuous wave. (ii) Magnetic induction is a double-edged sword for the treatment of PD. Although the increase of magnetic coefficient may lead the physiological firing activity to transfer to pathological firing activity, it also can regulate the pathological intensity firing activity with excessive β-band power transferring to the physiological firing pattern with weak β-band power. (iii) External magnetic radiation could inhibit continuous tremulous firing and β-band power of subthalamic nucleus (STN), which means the severity of symptoms weakened. Especially, the bi-parameter plane of the regulation region shows that a short pulse period of magnetic radiation and a medium level of pulse percentage can well regulate pathological oscillation. This work helps to understand the firing activity of the subthalamopallidal network under electromagnetic effect. It may also provide insights into the mechanisms behind the electromagnetic therapy of PD-related firing activity.     

Small-World Connections to Induce Firing Activity and Phase Synchronization in Neural Networks

We investigate how the firing activity and the subsequent phase synchronization of neural networks with smallworld topological connections depend on the probability p of adding-links. Network elements are described by two-dimensional map neurons （2DMNs） in a quiescent original state. Neurons burst for a given coupling strength when the topological randomness p increases, which is absent in a regular-lattice neural network. The bursting activity becomes frequent and synchronization of neurons emerges as topological randomness further increases. The maximal firing frequency and phase synchronization appear at a particular value of p. However, if the randomness p further increases, the firing frequency decreases and synchronization is apparently destroyed.     

Neuronal-like Irregular Spiking Dynamics in Highly Volatile Memristive Intermediate-scale AgPt-Nanoparticle Assemblies

Neuromorphic computing seeks functional materials capable of emulating brain-like dynamics to solve computational problems with time and energy efficiency, outclassing current transistor-based hardware architectures. Major efforts are focused on integrating memristive devices into highly regular circuits (i.e., crossbar arrays), where the information representation in individual memristive devices is closely oriented toward the behavior of artificial neurons. However, artificial neurons are rather rigid mathematical concepts than realistic projections of complex neuronal dynamics. Neuroscience suggests that highly efficient information representation on the level of individual neurons relies on dynamical features such as excitatory and inhibitory contributions, irregularity of firing patterns, and temporal correlations. Here, a conductive atomic force microscopy approach is applied to probe the memristive dynamics of nanoscale assemblies of AgPt-nanoparticles at the stability border of the conducting state, where physical forces causing the formation and decay of filamentary structures appear to be balanced. This unveils a dynamic regime, where the memristive response is governed by irregular firing patterns. The significance of such a dynamical regime is motivated by close similarities to excitation and inhibition-governed behavior in biological neuronal systems, which is crucial to tune biological neuronal systems into a state most suitable for information representation and computation.     

Firing Patterns and Transitions in Coupled Neurons Controlled by a Pacemaker

To reveal the dynamics of neuronal networks with pacemakers, the firing patterns and their transitions are investigated in a ring HR neuronal network with gap junctions under the control of a pacemaker. Compared with the situation without pacemaker, the neurons in the network can exhibit wrious firing patterns as the externed current is applied or the coupling strength of pacemaker varies. The results are beneficial for understanding the complex cooperative behaviour of large neural assemblies with pacemaker control.