首页> 外文期刊>Biological Cybernetics: Communication and Control in Organisms and Automata: = Nachrichtenubertragung, Nachrichtenverarbeitung, Steuerung und Regelung in Organismen und in Automaten >EXPERIMENTAL ANALYSIS OF NEURONAL DYNAMICS IN CULTURED CORTICAL NETWORKS AND TRANSITIONS BETWEEN DIFFERENT PATTERNS OF ACTIVITY
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EXPERIMENTAL ANALYSIS OF NEURONAL DYNAMICS IN CULTURED CORTICAL NETWORKS AND TRANSITIONS BETWEEN DIFFERENT PATTERNS OF ACTIVITY

机译:培养的皮质网络中神经元动力学的实验分析及不同活动模式之间的转换

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Experimental investigation of the dynamics of biological networks is a fundamental step towards understanding how the nervous system works. Spontaneous activity in cultured networks of cortical neurons has been investigated by using a multisite recording technique with planar electrode arrays. In these networks, the spatiotemporal firing patterns were studied in the presence of different extracellular solutions. Transitions from asynchronous firing dynamics to synchronous firing dynamics were observed when the extracellular Ca2+ concentration was increased from 0.1 mM to 1 mM. Addition of extracellular Mg2+ reduced the spontaneous activity at any Ca2+ concentration, and an increase in the extracellular K+ concentration enhanced the frequency of periodical synchronous bursts. N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptor antagonists inhibited synchronous activity, A spatiotemporal analysis of the data has been performed, and the properties of the network such as the synchronization and the periodicity have been quantified in order to clarify how variations of intrinsic parameters of the network can induce structural transitions in the neural dynamics. This experimental study is a possible approach to investigate the computational properties of a neuronal network. [References: 21]
机译:生物网络动力学的实验研究是了解神经系统如何工作的基本步骤。已通过使用具有平面电极阵列的多位记录技术研究了皮质神经元的培养网络中的自发活动。在这些网络中,在不同的细胞外溶液存在下研究了时空激发模式。当细胞外Ca2 +浓度从0.1 mM增加到1 mM时,观察到了从异步激发动力学到同步激发动力学的转变。在任何Ca2 +浓度下,添加细胞外Mg2 +都会降低自发活性,而细胞外K +浓度的增加会增加周期性同步爆发的频率。 N-甲基-D-天门冬氨酸(NMDA)和非NMDA谷氨酸受体拮抗剂抑制了同步活动,已对数据进行了时空分析,并对网络的属性(例如同步性和周期性)进行了量化,以便阐明网络固有参数的变化如何引起神经动力学的结构转变。该实验研究是研究神经元网络的计算特性的一种可能方法。 [参考:21]

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