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首页> 外文期刊>The European Journal of Neuroscience >The effects of dynamical synapses on firing rate activity: a spiking neural network model
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The effects of dynamical synapses on firing rate activity: a spiking neural network model

机译：动态突触对射击率活动的影响：尖峰神经网络模型

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Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABAA signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity.
机译：累积证据涉及突触成熟的微调和神经网络活动的调节到几个关键因素，包括GABAA信号传导和邻近神经元（即局部连接）之间的横向扩展长度。此外，许多研究将短期突触塑性（STP）视为神经网络中突触效能的即时改性的基本要素，以及调制对外部泊松输入频率（IF）的持续范围的响应。然而，响应于STP之间的动态相互作用（由IF）之间的动态相互作用和体外的这些关键参数来评估烧制活动仍然难以捉摸。因此，我们设计了一种尖峰神经网络（SNN）模型，其中我们纳入了以下参数：围绕神经元之间的局部密度和邻近神经元之间的横向扩展长度。我们还基于这些关键参数创建了多个网络方案。然后，我们实施了两类STP：（1）短期突触抑制（STD）和（2）短期突触促进（STF）。每个类基于其突触时间常数的参数值（用于按下或促进突触）。最后，我们在用STP治疗之前和之后进行了比较了神经烧制反应。我们发现动态突触（STP）对评估和调制每个网络场景中的射击率活动具有关键的差异作用。此外，我们调查了改变激发（E）和抑制（I）之间的平衡对稳定这种烧制活动的影响。

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