Institute Computational Physics for Engineering Materials, Eidgenössiche Technische Hochschule, Schafmattstrasse 6, 8093 Zürich, Switzerland.
Proc Natl Acad Sci U S A. 2010 Mar 2;107(9):3977-81. doi: 10.1073/pnas.0912289107. Epub 2010 Feb 16.
Recent physiological measurements have provided clear evidence about scale-free avalanche brain activity and EEG spectra, feeding the classical enigma of how such a chaotic system can ever learn or respond in a controlled and reproducible way. Models for learning, like neural networks or perceptrons, have traditionally avoided strong fluctuations. Conversely, we propose that brain activity having features typical of systems at a critical point represents a crucial ingredient for learning. We present here a study that provides unique insights toward the understanding of the problem. Our model is able to reproduce quantitatively the experimentally observed critical state of the brain and, at the same time, learns and remembers logical rules including the exclusive OR, which has posed difficulties to several previous attempts. We implement the model on a network with topological properties close to the functionality network in real brains. Learning occurs via plastic adaptation of synaptic strengths and exhibits universal features. We find that the learning performance and the average time required to learn are controlled by the strength of plastic adaptation, in a way independent of the specific task assigned to the system. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.
最近的生理测量提供了清晰的证据，证明了无标度的脑活动和 EEG 谱是混沌系统如何以可控和可重复的方式进行学习或响应的经典难题的关键所在。学习模型，如神经网络或感知器，传统上避免强烈的波动。相反，我们提出大脑活动具有临界点系统的特征，这是学习的关键因素。我们在这里提出了一项研究，为理解这个问题提供了独特的见解。我们的模型能够定量地再现大脑观察到的临界状态，同时学习和记忆逻辑规则，包括异或，这给之前的几次尝试带来了困难。我们在一个与真实大脑功能网络拓扑性质相近的网络上实现了这个模型。学习是通过突触强度的可塑性适应发生的，表现出普遍的特征。我们发现，学习性能和学习所需的平均时间由可塑性适应的强度控制，这种控制方式与系统分配的特定任务无关。即使是复杂的规则也可以被学习，只要可塑性适应足够慢。
