ETH Zürich, Computational Physics for Engineering Materials, Institute for Building Materials, Wolfgang-Pauli-Strasse 27, HIT, CH-8093 Zürich, Switzerland.
Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa (CE), Italy and INFN sez. Naples, Gr. Coll., Salerno, Italy.
Phys Rev E. 2018 Mar;97(3-1):032312. doi: 10.1103/PhysRevE.97.032312.
In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.
近年来，自组织临界神经元模型为理解神经元系统中实验观测到的雪崩行为的起源提供了新的视角。研究表明，动态突触作为一种短期可塑性，可以导致临界神经元动力学。而长期可塑性，如赫布或活动依赖性可塑性，在塑造网络结构和赋予神经系统学习能力方面起着关键作用。在这项工作中，我们提供了一个模型，该模型结合了两种可塑性机制，作用于两个不同的时间尺度。所测量的雪崩统计数据与实验结果相吻合，包括雪崩大小和持续时间分布，以及生物观测到的抑制性神经元百分比。神经元活动的时间序列表现出导致幂律衰减的时间突发，在功率谱中表现为 1/f 衰减。长期可塑性的存在使系统能够学习二进制规则，如异或，为未来更复杂任务（如模式识别）的研究提供了基础。
