Self-sustained activity of low firing rate in balanced networks
Self-sustained activity of low firing rate in balanced networks
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DOI：
10.1016/j.physa.2019.122671
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发表时间：
2020-01-01
影响因子：
3.3
通讯作者：
Batista, A. M.
中科院分区：
文献类型：
--
作者：
Borges, F. S.;Protachevicz, P. R.;Batista, A. M.
在没有外部刺激的情况下观察到大脑中的自我维持活动，并有助于信号传播，神经编码和动态稳定性。它在认知过程中也起着重要作用。在这项工作中，通过研究大鼠CA1神经元的细胞内记录和数值模拟的结果，我们证明了自我维持的活动呈现出高度的模式可变性，例如低神经放电率和不同神经元中小爆发形式的活动。在我们的数值模拟中，我们考虑由耦合的自适应指数积分和激发神经元组成的随机网络。随机网络中的神经动力学模拟规则尖峰（兴奋性）和快速尖峰（抑制性）神经元。我们表明，连接概率和网络大小的基本属性，引起自我维持的活动与我们的实验结果定性一致。最后，我们提供了一个更详细的描述自我维持的活动的寿命分布，突触电导和突触电流。(C)2019爱思唯尔B.V.保留所有权利。
Self-sustained activity in the brain is observed in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes. In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulates regular spiking (excitatory) and fast spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents. (C) 2019 Elsevier B.V. All rights reserved.