Adaptive self-organization in a realistic neural network model
Adaptive self-organization in a realistic neural network model
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DOI：
10.1103/physreve.80.061917
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发表时间：
2009-12-01
影响因子：
2.4
通讯作者：
Gross, Thilo
中科院分区：
文献类型：
--
作者：
Meisel, Christian;Gross, Thilo
在复杂系统中，信息处理常常在接近与相变相关的临界状态时达到最高效率。因此可以想象，神经信息处理也是在接近临界状态下运行的。对幂律分布的观察进一步支持了这一点，幂律分布是相变的一个标志。一个重要的未解决问题是，神经网络在发育、适应、学习等过程中不断变化的情况下如何能保持接近临界点。博恩霍尔特（Bornholdt）和罗尔夫（Rohlf）做出了有影响力的贡献，他们引入了自适应网络中稳健的自组织临界性的一般机制。在此，我们探讨这个机制是否与真实的神经网络相关这一问题。我们在一个现实的模型中表明，依赖于尖峰时间的突触可塑性能够使神经网络稳健地自组织向临界状态。我们的模型重现了一些实证观察结果，并对突触强度的分布做出了可检验的预测，将它们与网络的临界状态联系起来。这些结果表明，动力学和拓扑结构之间的相互作用对于神经信息处理可能是至关重要的。
Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.