有哪些信誉好的足球投注网站a homeostatic model of neuronal firing governed by feedback signals from the extracellular matrix神经元活动的稳态模型由反馈信号从细胞外基质.pdfVIP
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a homeostatic model of neuronal firing governed by feedback signals from the extracellular matrix神经元活动的稳态模型由反馈信号从细胞外基质
A Homeostatic Model of Neuronal Firing Governed by Feedback Signals from the Extracellular Matrix Victor Kazantsev1,2*, Susan Gordleeva1,2, Sergey Stasenko1,2, Alexander Dityatev2,3,4 1 Laboratory of Nonlinear Dynamics of Living Systems, Institute of Applied Physics of Russian Academy of Science, Nizhny Novgorod, Russia, 2 Laboratory for Brain Extracellular Matrix Research, University of Nizhny Novgorod, Nizhny Novgorod, Russia, 3 Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy, 4 Molecular Neuroplasticity Group, DZNE, German Center for Neurodegenerative Diseases, Magdeburg, Germany Abstract Molecules of the extracellular matrix (ECM) can modulate the efficacy of synaptic transmission and neuronal excitability. These mechanisms are crucial for the homeostatic regulation of neuronal firing over extended timescales. In this study, we introduce a simple mathematical model of neuronal spiking balanced by the influence of the ECM. We consider a neuron receiving random synaptic input in the form of Poisson spike trains and the ECM, which is modeled by a phenomenological variable involved in two feedback mechanisms. One feedback mechanism scales the values of the input synaptic conductance to compensate for changes in firing rate. The second feedback accounts for slow fluctuations of the excitation threshold and depends on the ECM concentration. We show that the ECM-mediated feedback acts as a robust mechanism to provide a homeostatic adjustment of the average firing rate. Interestingly, the activation of feedback mechanisms may lead to a bistability in which two different stable levels of average firing rates can coexist in a spiking network. We discuss the mechanisms of the bistability and how they may be related to memory function. Citation: Kazantsev V, Gord
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