Lobov Sergey A, Berdnikova Ekaterina S, Zharinov Alexey I, Kurganov Dmitry P, Kazantsev Victor B
Laboratory of Neurobiomorphic Technologies, The Moscow Institute of Physics and Technology, 117303 Moscow, Russia.
Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia.
Biomimetics (Basel). 2023 Jul 20;8(3):320. doi: 10.3390/biomimetics8030320.
Mathematical and computer simulation of learning in living neural networks have typically focused on changes in the efficiency of synaptic connections represented by synaptic weights in the models. Synaptic plasticity is believed to be the cellular basis for learning and memory. In spiking neural networks composed of dynamical spiking units, a biologically relevant learning rule is based on the so-called spike-timing-dependent plasticity or STDP. However, experimental data suggest that synaptic plasticity is only a part of brain circuit plasticity, which also includes homeostatic and structural plasticity. A model of structural plasticity proposed in this study is based on the activity-dependent appearance and disappearance of synaptic connections. The results of the research indicate that such adaptive rewiring enables the consolidation of the effects of STDP in response to a local external stimulation of a neural network. Subsequently, a vector field approach is used to demonstrate the successive "recording" of spike paths in both functional connectome and synaptic connectome, and finally in the anatomical connectome of the network. Moreover, the findings suggest that the adaptive rewiring could stabilize network dynamics over time in the context of activity patterns' reproducibility. A universal measure of such reproducibility introduced in this article is based on similarity between time-consequent patterns of the special vector fields characterizing both functional and anatomical connectomes.
对活体神经网络学习的数学和计算机模拟通常聚焦于模型中由突触权重表示的突触连接效率的变化。突触可塑性被认为是学习和记忆的细胞基础。在由动态发放单元组成的脉冲神经网络中，一种与生物学相关的学习规则基于所谓的脉冲时间依赖可塑性（STDP）。然而，实验数据表明突触可塑性只是脑回路可塑性的一部分，脑回路可塑性还包括稳态可塑性和结构可塑性。本研究中提出的一种结构可塑性模型基于突触连接的活动依赖性出现和消失。研究结果表明，这种适应性重连能够巩固STDP对神经网络局部外部刺激的响应效果。随后，采用矢量场方法来展示在功能连接组、突触连接组以及最终在网络的解剖连接组中脉冲路径的连续“记录”。此外，研究结果表明，在活动模式可重复性的背景下，适应性重连能够随着时间推移稳定网络动态。本文引入的这种可重复性的通用度量基于表征功能连接组和解剖连接组的特殊矢量场的时间序列模式之间的相似性。
