Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network m...Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network model is subject to spike noise with exponentially distributed interspike intervals.The excitatory(E)and/or inhibitory(I)neurons interact through synapses whose weights show a log-normal distribution.The spike behavior observed in the network model with the appropriate log-normal distributed synaptic weights fits best to that observed in the experiment.The best-fit is then achieved with high activities of I neurons having a hub-like structure,in which the I neurons,subject to optimized spike noise,are intensively projected from low active E neurons.展开更多
The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dyn...The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dynamical mechanisms of generation of neural oscillations,and the neural temperature is widely known to significantly affect the oscillations.Nevertheless,little is discussed about how the temperature affects the f–I curve and PRC.In this study,frequencies of the neural oscillations,modulated with the temperature variations,are quantified with an average of the PRC.The frequency gradient with respect to temperature derived here gives clear classifications of the PRC,regardless of the form.It is also indicated that frequency decreases with an increase in temperature,resulted from bifurcation switching of the saddle-homoclinic to the saddle-node on an invariant circle.展开更多
We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose （2dHR） oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf （All） type to the subcr...We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose （2dHR） oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf （All） type to the subcritical Andronov-Hopf （SAH） type. We identify dynamical mechanisms which are responsible for changes of the repeti- tive firing rate during the AH to SAH bifurcation transitions. These include frequency-shift functions in response to small perturbations of a timescale parameter, its multiplicative parameter, and an external input current in the 2dHR oscillatory system. The frequency-shift functions are explicitly represented as functions relating to the phase response curves （PRCs）. Then, we demonstrate that when the timescale is normal and relatively fast, the repetitive firing rate slightly increases and decreases respectively during the AH to SAH bifurcation transition with a change of the intrinsic parameter, whereas it decreases during the SAH to AH bifurcation transition with an increase in the timescale. By analyzing the three different frequency-shift functions, we show that such changes of the repetitive firing rate depend largely on changes of the PRC size. The PRC size for the SAH bifurcation shrinks to the PRC size for the AH bifurcation.展开更多
A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophistic...A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments. We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems： an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map （SOM） for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot. The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.展开更多
基金Supported by the Grant-in-Aid for Challenging Exploratory Research(No 25540110)from MEXT.
文摘Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network model is subject to spike noise with exponentially distributed interspike intervals.The excitatory(E)and/or inhibitory(I)neurons interact through synapses whose weights show a log-normal distribution.The spike behavior observed in the network model with the appropriate log-normal distributed synaptic weights fits best to that observed in the experiment.The best-fit is then achieved with high activities of I neurons having a hub-like structure,in which the I neurons,subject to optimized spike noise,are intensively projected from low active E neurons.
基金Supported by the Grant-in-Aid for Challenging Exploratory Research from MEXT(No 25540110).
文摘The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dynamical mechanisms of generation of neural oscillations,and the neural temperature is widely known to significantly affect the oscillations.Nevertheless,little is discussed about how the temperature affects the f–I curve and PRC.In this study,frequencies of the neural oscillations,modulated with the temperature variations,are quantified with an average of the PRC.The frequency gradient with respect to temperature derived here gives clear classifications of the PRC,regardless of the form.It is also indicated that frequency decreases with an increase in temperature,resulted from bifurcation switching of the saddle-homoclinic to the saddle-node on an invariant circle.
文摘We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose （2dHR） oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf （All） type to the subcritical Andronov-Hopf （SAH） type. We identify dynamical mechanisms which are responsible for changes of the repeti- tive firing rate during the AH to SAH bifurcation transitions. These include frequency-shift functions in response to small perturbations of a timescale parameter, its multiplicative parameter, and an external input current in the 2dHR oscillatory system. The frequency-shift functions are explicitly represented as functions relating to the phase response curves （PRCs）. Then, we demonstrate that when the timescale is normal and relatively fast, the repetitive firing rate slightly increases and decreases respectively during the AH to SAH bifurcation transition with a change of the intrinsic parameter, whereas it decreases during the SAH to AH bifurcation transition with an increase in the timescale. By analyzing the three different frequency-shift functions, we show that such changes of the repetitive firing rate depend largely on changes of the PRC size. The PRC size for the SAH bifurcation shrinks to the PRC size for the AH bifurcation.
文摘A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments. We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems： an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map （SOM） for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot. The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.
