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Growth Dynamics Explain the Development of Spatiotemporal Burst Activity of Young Cultured Neuronal Networks in Detail

机译:生长动力学详细解释了培养的神经元网络的时空爆发活动的发展。

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摘要

A typical property of isolated cultured neuronal networks of dissociated rat cortical cells is synchronized spiking, called bursting, starting about one week after plating, when the dissociated cells have sufficiently sent out their neurites and formed enough synaptic connections. This paper is the third in a series of three on simulation models of cultured networks. Our two previous studies [26], [27] have shown that random recurrent network activity models generate intra- and inter-bursting patterns similar to experimental data. The networks were noise or pacemaker-driven and had Izhikevich-neuronal elements with only short-term plastic (STP) synapses (so, no long-term potentiation, LTP, or depression, LTD, was included). However, elevated pre-phases (burst leaders) and after-phases of burst main shapes, that usually arise during the development of the network, were not yet simulated in sufficient detail. This lack of detail may be due to the fact that the random models completely missed network topology .and a growth model. Therefore, the present paper adds, for the first time, a growth model to the activity model, to give the network a time dependent topology and to explain burst shapes in more detail. Again, without LTP or LTD mechanisms. The integrated growth-activity model yielded realistic bursting patterns. The automatic adjustment of various mutually interdependent network parameters is one of the major advantages of our current approach. Spatio-temporal bursting activity was validated against experiment. Depending on network size, wave reverberation mechanisms were seen along the network boundaries, which may explain the generation of phases of elevated firing before and after the main phase of the burst shape.In summary, the results show that adding topology and growth explain burst shapes in great detail and suggest that young networks still lack/do not need LTP or LTD mechanisms.
机译:分离的大鼠皮层细胞的分离培养的神经元网络的典型特性是同步刺突,称为爆发,始于接种后约一周,此时分离的细胞已充分发出神经突并形成足够的突触连接。本文是关于培养网络的三个仿真模型系列的第三篇。我们之前的两项研究[26],[27]已表明,随机循环网络活动模型生成的突发内和突发间模式与实验数据相似。该网络是由噪声或起搏器驱动的,并且具有仅短期塑料(STP)突触的伊热克维奇神经元(因此,不包括长期增强,LTP或抑郁症,LTD)。但是,尚未充分详细地模拟通常在网络开发过程中出现的突发主要形状的升高的前期(爆发前导)和后期。缺乏细节可能是由于以下事实:随机模型完全错过了网络拓扑和增长模型。因此,本论文首次将增长模型添加到活动模型中,为网络提供了时间依赖性拓扑,并更详细地解释了突发形状。同样,没有LTP或LTD机制。综合的增长活动模型产生了现实的爆发模式。自动调整各种相互依赖的网络参数是我们当前方法的主要优势之一。时空爆发活动已针对实验进行了验证。根据网络的大小,在网络边界上看到了波混响机制,这可以解释突发形状主相位之前和之后高发射的相位的产生。总而言之,结果表明添加拓扑和增长可以解释突发形状详细地说明,年轻的网络仍然缺少/不需要LTP或LTD机制。

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