Despite recognized layered structure and increasing evidence for criticality in the cortex, how the specification of input, output and computational layers affects the self-organized criticality has been surprisingly neglected. By constructing heterogeneous structures with a well-accepted model of leaky neurons, we found that the specification can lead to robust criticality almost insensitive to the strength of external stimuli. This naturally unifies the adaptation to strong inputs without extra synaptic plasticity mechanisms. Presence of output neurons constitutes an alternative explanation to subcriticality other than the high frequency inputs. Degree of recurrence is proposed as a network metric to account for the signal termination due to output neurons. Unlike fully recurrent networks where external stimuli always render subcriticality, the dynamics of networks with sufficient feed-forward connections can be driven to criticality and supercriticality. These findings indicate that functional and structural specification and their interplay with external stimuli are of crucial importance for the network dynamics. The robust criticality puts forward networks of the leaky neurons as a promising platform for realizing artificial neural networks that work in the vicinity of critical points.

中文翻译：

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功能规范导致的鲁棒皮质临界性和不同的神经网络动力学

尽管公认的分层结构和越来越多的证据表明皮质具有关键性，但令人惊讶地忽略了输入，输出和计算层的规范如何影响自组织关键性。通过使用公认的泄漏神经元模型构建异质结构，我们发现该规范可导致对外部刺激强度几乎不敏感的鲁棒性临界。这自然会统一对强输入的适应，而无需额外的突触可塑性机制。除了高频输入外，输出神经元的存在构成了对次临界状态的另一种解释。递归度被建议作为一种网络度量标准，以说明由于输出神经元而导致的信号终止。与完全循环的网络（外部刺激总是使次临界状态）不同，具有足够的前馈连接的网络动态可以被驱动到临界和超临界状态。这些发现表明功能和结构规格及其与外部刺激的相互作用对于网络动力学至关重要。鲁棒的临界度提出了泄漏神经元的网络，作为实现在临界点附近工作的人工神经网络的有希望的平台。