Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization i...Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network, Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.展开更多
针对高动态环境下突发信号检测问题,提出基于高阶项逐级消去的非线性调频(non-linear frequency modulation,NLFM)信号参数估计算法,利用合理近似,将其转化为相对简单的线性调频(linear frequency modulation,LFM)信号参数估计问题,并...针对高动态环境下突发信号检测问题,提出基于高阶项逐级消去的非线性调频(non-linear frequency modulation,NLFM)信号参数估计算法,利用合理近似,将其转化为相对简单的线性调频(linear frequency modulation,LFM)信号参数估计问题,并提出两级调频率逼近法用于LFM信号参数估计,具有原理简明、计算复杂度低等特点,便于实际工程应用.针对接收信号功率动态变化的问题,提出自适应快速傅里叶变换(fast Fourier transform,FFT)峰均比门限信号检测算法.仿真结果表明,所提算法能够在信噪比为-27dB的高动态环境下准确实现信号检测与参数估计.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61072012, 61104032, and 61172009)the Natural Science Foundation of Tianjin Municipality, China (Grant No. 12JCZDJC21100)the Young Scientists Fund of the National Natural Science Foundation of China (GrantNos. 60901035 and 50907044)
文摘Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network, Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.
文摘针对高动态环境下突发信号检测问题,提出基于高阶项逐级消去的非线性调频(non-linear frequency modulation,NLFM)信号参数估计算法,利用合理近似,将其转化为相对简单的线性调频(linear frequency modulation,LFM)信号参数估计问题,并提出两级调频率逼近法用于LFM信号参数估计,具有原理简明、计算复杂度低等特点,便于实际工程应用.针对接收信号功率动态变化的问题,提出自适应快速傅里叶变换(fast Fourier transform,FFT)峰均比门限信号检测算法.仿真结果表明,所提算法能够在信噪比为-27dB的高动态环境下准确实现信号检测与参数估计.
