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Synchronization effects using a piecewise linear map-based spiking-bursting neuron model

机译:使用基于分段线性映射的突增突发神经元模型的同步效果

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Models of neurons based on iterative maps allows the simulation of big networks of coupled neurons without loss of biophysical properties such as spiking, bursting or tonic bursting and with an affordable computational effort. These models are built over a phenomenological basis and are mainly implemented by the use of iterative two-dimensional maps that can present neuro-computational properties similar to the usual differential models. A piecewise linear two-dimensional map with one fast and one slow coupled variables is used to model spiking-bursting neural behavior. This map shows oscillations similar to other phenomenological models based on maps that require a higher computational effort. The dynamics of coupled neurons is studied for different coupling strengths and the formation of spatio-temporal patterns of neuronal activity is also explored.
机译:基于迭代图的神经元模型可以模拟耦合神经元的大型网络,而不会损失诸如尖峰,爆发或强音爆发之类的生物物理特性,并且具有可承受的计算量。这些模型是建立在现象学基础上的,主要是通过使用迭代二维映射来实现的,该二维映射可以呈现类似于通常的差分模型的神经计算特性。具有一个快速耦合变量和一个慢速耦合变量的分段线性二维映射用于对突跳神经行为进行建模。该图显示了与基于需要更高计算量的图的其他现象学模型相似的振荡。针对不同的耦合强度研究了耦合神经元的动力学,并探讨了神经元活动的时空模式的形成。

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