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首页> 外文期刊>Physical review, E >Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neurons
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Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neurons

机译：抑制性神经元促进整合和防火神经元网络中的强大临界射击动力学

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摘要

We study the firing dynamics of a discrete-state and discrete-time version of an integrate-and-fire neuronal network model with both excitatory and inhibitory neurons. When the integer-valued state of a neuron exceeds a threshold value, the neuron fires, sends out state-changing signals to its connected neurons, and returns to the resting state. In this model, a continuous phase transition from non-ceaseless firing to ceaseless firing is observed. At criticality, power-law distributions of avalanche size and duration with the previously derived exponents, -3/2 and -2, respectively, are observed. Using a mean-field approach, we show analytically how the critical point depends on model parameters. Our main result is that the combined presence of both inhibitory neurons and integrate-and-fire dynamics greatly enhances the robustness of critical power-law behavior (i. e., there is an increased range of parameters, including both sub-and supercritical values, for which several decades of power-law behavior occurs).
机译：我们研究了兴奋性和抑制性神经元的集成和灭火神经元网络模型的离散状态和离散时间版本的射击动态。当神经元的整数值超过阈值时，神经元触发，向其连接的神经元发送状态改变信号，并返回到静止状态。在该模型中，观察到从非无需射击到不可行的射击的连续相位过渡。观察到临界，分别观察到雪崩大小和持续时间，分别具有先前推导的指数-3 / 2和-2的血管规模和持续时间。使用平均场方法，我们在分析上显示了临界点如何取决于模型参数。我们的主要结果是，抑制性神经元和集成和消防动力学的结合存在大大提高了临界权力法行为的鲁棒性（即，参数增加，包括子和超临界值发生了几十年的权力法行为）。

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