《运动解剖学》课程文献资料（脑科学）基于突触可塑性的复杂系统中信息传输机制的研究.pdf_P6-P10

同步程度显著提高，并且γ波段节律与同步程度的变化趋向于一致。其次，CH放电模式的神经元可以更有效诱导网络系统实现完全同步，并且网络对控制参数的变化更加敏感。最后，采用混合的具有Regular Spiking (RS)放电模式的神经元与具有Chattering(CH)放电模式的神经元来共同模拟兴奋性神经元，而同样具有Fast Spiking(FS)放电模式的神经元在整个神经网络中始终扮演抑制性神经元的角色。研究结果发现具有簇放电模式神经元的神经网络比具有规律峰放电模式神经元的神经网络更容易达到完全同步，并且更容易受到参数变化的影响。 3、综合地探讨了一个由兴奋性(E)锥体神经元和抑制性()中间神经元组成的复杂网络，研究spike-timing-dependent plasity(STDP)可塑性与化学突触之间的相互作用对网络同步和震荡表现的影响。为了探索兴奋突触可塑性(eSTDP)以及抑制突触可塑性(STDP)在兴奋抑制平衡神经网络中的同步与振荡行为，综合性分析了三种不同的模型，包括纯粹由兴奋性锥体神经元与STDP兴奋性突触学习机制构成的复杂网络，由纯粹的抑制性中间神经元和STDP抑制性突触学习规则组成的复杂网络，以及由兴奋抑制平衡神经元网络结合混合突触可塑性建立的复杂网络集群模型。通过改变抑制中间神经元的数量、小世界网络拓扑结构中连接边的数量和耦合强度，这三个网络表现出不同的同步与振荡行为。此外，研究发现，eSTDP可以有效地促进同步，而iSTDP对同步没有显著的影响。有趣的是，在兴奋-抑制平衡神经网络中，eSTDP和iSTDP共同作用、互相制约，在调控网络平衡与稳定方面发挥着重要的作用关键词：神经网络小世界网络同步节律兴奋抑制平衡网络突触可塑性信息传递机制 (C)1994-2021 China Academic Joumal Electronic Publishing House.All rights reserved.http:/www.cnki.net

同步程度显著提高，并且ｙ波段节律与同步程度的变化趋向于一致。其次，ＣＨ放电模式的神经元可以更有效诱导网络系统实现完全同步，并且网络对控制参数的变化更加敏感。最后，采用混合的具有ＲｅｇｕｌａｒＳｐｉｋｉｎｇ（ＲＳ）放电模式的神经元与具有Ｃｈａｔｔｅｒｉｎｇ（ＣＨ）放电模式的神经兀来共同模拟兴奋性神经元，而同样具有ＦａｓｔＳｐｉｋｉｎｇ（ＦＳ）放电模式的神经元在整个神经网络中始终扮演抑制性神经元的角色。研究结果发现具有簇放电模式神经元的神经网络比具有规律峰放电模式神经元的神经网络更容易达到完全同步，并且更容易受到参数变化的影响。３、综合地探讨了一个由兴奋性（Ｅ）锥体神经元和抑制性（Ｉ）中间神经元组成的复杂网络，研究ｓｐｉｋｅ－ｔｉｍｉｎｇ－ｄｅｐｅｎｄｅｎｔｐｌａｓｉｔｙ（ＳＴＤＰ）可塑性与化学突触之间的相互作用对网络同步和震荡表现的影响。为了探索兴奋突触可塑性（ｅＳＴＤＰ）以及抑制突触可塑性（ｉＳＴＤＰ）在兴奋抑制平衡神经网络中的同步与振荡行为，综合性分析了三种不同的模型，包括纯粹由兴奋性锥体神经元与ｅＳＴＤＰ兴奋性突触学习机制构成的复杂网络，由纯粹的抑制性中间神经元和ｉＳＴＤＰ抑制性突触学习规则组成的复杂网络，以及由兴奋抑制平衡神经元网络结合混合突触可塑性建立的复杂网络集群模型。通过改变抑制中间神经元的数量、小世界网络拓扑结构中连接边的数量和耦合强度，这三个网络表现出不同的同步与振荡行为。此外，研究发现，ｅＳＴＤＰ可以有效地促进同步，而ｉＳＴＤＰ对同步没有显著的影响。有趣的是，在兴奋－抑制平衡神经网络中，ｅＳＴＤＰ和ｉＳＴＤＰ共同作用、互相制约，在调控网络平衡与稳定方面发挥着重要的作用。关键词：神经网络小世界网络同步节律兴奋抑制平衡网络突触可塑性信息传递机制ＩＩ

SIGNAL TRANSMISSION WITH SYNAPTIC PLASTICITY IN COMPLEX NEURAL SYSTEMS ABSTRACT The transmission ofinformation in the cerebral cortex is an extremely com- plex process,and how to integrate and process neural information in the brain is a major challenge for the scientific community in the 21st century.Compu tational neuroscience,as a discipline that combines the theories and knowledge of biology,physics,mathematics and computer science,has opened up a new path for understanding the information transmission mechanism of the brain. Synaptic plasticity plays an especial role in the cognitive process of biological brain,which is closely related to human learning,memory,reasoning and other advanced cognitive functions.Therefore,the study of the influence of synaptic plasticity on the mechanism of information transmission in the nervous system has been a hot topic in recent years. In this study,we focused on the effects of synaptic plasticity on synchro- nization and neural rhythm in two coupled neurons,a small-world neural net- work and an excitation-inhibition balanced neural network by establishing math- ematical models and computer simulation methods.Through the combination of theory and experiment,the main work and conclusions of this paper are sum- marized as follows: 1.The characteristics of cluster firing synchronization behaviors and the neural rhythm transition process of two Hindmarsh-Rose neurons under the in- fluence of synaptic plasticity were studied in depth,including electrical synapses and chemical synapses.At the same time,a digital characteristic parameter,the synchronization window,is creatively introduced to measure the synchroniza- tion characteristics of cluster firing neurons.The effects of coupling strength and learning rate on synchronization of coupled neurons and synaptic plasticity on rhythm transition of coupled neurons were investigated.The results show C)1994-2021 China Academic Joumal Electronic Publishing House.All rights reserved. /www.cnki.net

ＳＩＧＮＡＬＴＲＡＮＳＭＩＳＳＩＯＮＷＩＴＨＳＹＮＡＰＴＩＣＰＬＡＳＴＩＣＩＴＹＩＮＣＯＭＰＬＥＸＮＥＵＲＡＬＳＹＳＴＥＭＳＡＢＳＴＲＡＣＴＴｈｅｔｒａｎｓｍｉｓｓｉｏｎｏｆｉｎｆｏｒｍａｔｉｏｎｉｎｔｈｅｃｅｒｅｂｒａｌｃｏｒｔｅｘｉｓａｎｅｘｔｒｅｍｅｌｙｃｏｍ？ｐｌｅｘｐｒｏｃｅｓｓ，ａｎｄｈｏｗｔｏｉｎｔｅｇｒａｔｅａｎｄｐｒｏｃｅｓｓｎｅｕｒａｌｉｎｆｏｒｍａｔｉｏｎｉｎｔｈｅｂｒａｉｎｉｓａｍａｊｏｒｃｈａｌｌｅｎｇｅｆｏｒｔｈｅｓｃｉｅｎｔｉｆｉｃｃｏｍｍｕｎｉｔｙｉｎｔｈｅ２１ｓｔｃｅｎｔｕｒｙ．Ｃｏｍｐｕ？ｔａｔｉｏｎａｌｎｅｕｒｏｓｃｉｅｎｃｅ，ａｓａｄｉｓｃｉｐｌｉｎｅｔｈａｔｃｏｍｂｉｎｅｓｔｈｅｔｈｅｏｒｉｅｓａｎｄｋｎｏｗｌｅｄｇｅｏｆｂｉｏｌｏｇｙ，ｐｈｙｓｉｃｓ，ｍａｔｈｅｍａｔｉｃｓａｎｄｃｏｍｐｕｔｅｒｓｃｉｅｎｃｅ，ｈａｓｏｐｅｎｅｄｕｐａｎｅｗｐａｔｈｆｏｒｕｎｄｅｒｓｔａｎｄｉｎｇｔｈｅｉｎｆｏｒｍａｔｉｏｎｔｒａｎｓｍｉｓｓｉｏｎｍｅｃｈａｎｉｓｍｏｆｔｈｅｂｒａｉｎ．Ｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙｐｌａｙｓａｎｅｓｐｅｃｉａｌｒｏｌｅｉｎｔｈｅｃｏｇｎｉｔｉｖｅｐｒｏｃｅｓｓｏｆｂｉｏｌｏｇｉｃａｌｂｒａｉｎ，ｗｈｉｃｈｉｓｃｌｏｓｅｌｙｒｅｌａｔｅｄｔｏｈｕｍａｎｌｅａｒｎｉｎｇ，ｍｅｍｏｒｙ，ｒｅａｓｏｎｉｎｇａｎｄｏｔｈｅｒａｄｖａｎｃｅｄｃｏｇｎｉｔｉｖｅｆｕｎｃｔｉｏｎｓ．Ｔｈｅｒｅｆｏｒｅ，ｔｈｅｓｔｕｄｙｏｆｔｈｅｉｎｆｌｕｅｎｃｅｏｆｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙｏｎｔｈｅｍｅｃｈａｎｉｓｍｏｆｉｎｆｏｒｍａｔｉｏｎｔｒａｎｓｍｉｓｓｉｏｎｉｎｔｈｅｎｅｒｖｏｕｓｓｙｓｔｅｍｈａｓｂｅｅｎａｈｏｔｔｏｐｉｃｉｎｒｅｃｅｎｔｙｅａｒｓ．Ｉｎｔｈｉｓｓｔｕｄｙ，ｗｅｆｏｃｕｓｅｄｏｎｔｈｅｅｆｆｅｃｔｓｏｆｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙｏｎｓｙｎｃｈｒｏ？ｎｉｚａｔｉｏｎａｎｄｎｅｕｒａｌｒｈｙｔｈｍｉｎｔｗｏｃｏｕｐｌｅｄｎｅｕｒｏｎｓ，ａｓｍａｌｌ－ｗｏｒｌｄｎｅｕｒａｌｎｅｔ？ｗｏｒｋａｎｄａｎｅｘｃｉｔａｔｉｏｎ－ｉｎｈｉｂｉｔｉｏｎｂａｌａｎｃｅｄｎｅｕｒａｌｎｅｔｗｏｒｋｂｙｅｓｔａｂｌｉｓｈｉｎｇｍａｔｈ－ｅｍａｔｉｃａｌｍｏｄｅｌｓａｎｄｃｏｍｐｕｔｅｒｓｉｍｕｌａｔｉｏｎｍｅｔｈｏｄｓ．Ｔｈｒｏｕｇｈｔｈｅｃｏｍｂｉｎａｔｉｏｎｏｆｔｈｅｏｒｙａｎｄｅｘｐｅｒｉｍｅｎｔ，ｔｈｅｍａｉｎｗｏｒｋａｎｄｃｏｎｃｌｕｓｉｏｎｓｏｆｔｈｉｓｐａｐｅｒａｒｅｓｕｍ？ｍａｒｉｚｅｄａｓｆｏｌｌｏｗｓ：１．ＴｈｅｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｃｌｕｓｔｅｒｆｉｒｉｎｇｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｂｅｈａｖｉｏｒｓａｎｄｔｈｅｎｅｕｒａｌｒｈｙｔｈｍｔｒａｎｓｉｔｉｏｎｐｒｏｃｅｓｓｏｆｔｗｏＨｉｎｄｍａｒｓｈ－Ｒｏｓｅｎｅｕｒｏｎｓｕｎｄｅｒｔｈｅｉｎ？ｆｌｕｅｎｃｅｏｆｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙｗｅｒｅｓｔｕｄｉｅｄｉｎｄｅｐｔｈ，ｉｎｃｌｕｄｉｎｇｅｌｅｃｔｒｉｃａｌｓｙｎａｐｓｅｓａｎｄｃｈｅｍｉｃａｌｓｙｎａｐｓｅｓ．Ａｔｔｈｅｓａｍｅｔｉｍｅ，ａｄｉｇｉｔａｌｃｈａｒａｃｔｅｒｉｓｔｉｃｐａｒａｍｅｔｅｒ，ｔｈｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｗｉｎｄｏｗ，ｉｓｃｒｅａｔｉｖｅｌｙｉｎｔｒｏｄｕｃｅｄｔｏｍｅａｓｕｒｅｔｈｅｓｙｎｃｈｒｏｎｉｚａ？ｔｉｏｎｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｃｌｕｓｔｅｒｆｉｒｉｎｇｎｅｕｒｏｎｓ．Ｔｈｅｅｆｆｅｃｔｓｏｆｃｏｕｐｌｉｎｇｓｔｒｅｎｇｔｈａｎｄｌｅａｒｎｉｎｇｒａｔｅｏｎｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｏｆｃｏｕｐｌｅｄｎｅｕｒｏｎｓａｎｄｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙｏｎｒｈｙｔｈｍｔｒａｎｓｉｔｉｏｎｏｆｃｏｕｐｌｅｄｎｅｕｒｏｎｓｗｅｒｅｉｎｖｅｓｔｉｇａｔｅｄ．Ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｈｉ

that electrical synaptic coupling can enhance the synchrony ofneurons.In addi- tion,there is a close correlation between spike and cluster synchronization and the initial firing state of neurons.In the case of chemical synaptic coupling, no matter the coupling type is excitatory or inhibitory,increasing the coupling strength can promote the spike synchronization of neurons,but the synchronous change of excitatory chemical synaptic coupling state is more obvious. 2.The changes of y band rhythm and synchronization transition in small- world neural networks were further investigated.Using Izhikevich neurons withdere firing patters,acomplex network model withasmall-worldnet- work topology was constructed to simulate thethe cerebral cortex.The results show that the degree of synchronization was significantly increased with the increase of the weight of synaptic connections and the number of the nearest neighbors of each node,and the variation of the y band rhythm tends to be con- sistent with the degree of synchronization.Secondly,Chattering neurons can induce the network to achieve complete synchronization more effectively,and it is more sensitive to the change of control parameters.Finally,Regular Spik- ing neurons and Chattering neurons were used to jointly simulate excitatory neurons,while Fast Spiking neurons always mimic inhibitory neurons in the neural network.The results show that the neural network with chattering neu- rons is more likely to achieve complete synchronization than those with regular spiking neurons,and is more likely to be affected by parameter changes. 3.A complex network consisting of excitatory(E)pyramidal neurons and inhibitory (I)interneurons was investigated,and the effects of the interaction between spike-timing-dependent plasticity and chemical synapses on network synchronization and oscillation behaviors were investigated.To explore the ef fects of eSTDP and iSTDP on thethe synchronous and oscillatory behaviors in excitatory inhibitory balanced cortical neural network,we compared and analyzed three models.The first is a complex network composed purely of excitatory pyramidal neurons and eSTDP excitatory synaptic learning mecha- nisms,and the second is a complex network composed of pure inhibitory in- IV (C)1994-2021 China Academie Joumal Electronic Publishing House.All rights reserved.http:/www.cnki.net

ｔｈａｔｅｌｅｃｔｒｉｃａｌｓｙｎａｐｔｉｃｃｏｕｐｌｉｎｇｃａｎｅｎｈａｎｃｅｔｈｅｓｙｎｃｈｒｏｎｙｏｆｎｅｕｒｏｎｓ．Ｉｎａｄｄｉ？ｔｉｏｎ，ｔｈｅｒｅｉｓａｃｌｏｓｅｃｏｒｒｅｌａｔｉｏｎｂｅｔｗｅｅｎｓｐｉｋｅａｎｄｃｌｕｓｔｅｒｓｙｎｃｈｒｏｎｉｚａｔｉｏｎａｎｄｔｈｅｉｎｉｔｉａｌｆｉｒｉｎｇｓｔａｔｅｏｆｎｅｕｒｏｎｓ．Ｉｎｔｈｅｃａｓｅｏｆｃｈｅｍｉｃａｌｓｙｎａｐｔｉｃｃｏｕｐｌｉｎｇ，ｎｏｍａｔｅｒｔｈｅｃｏｕｐｌｉｎｇｔｙｐｅｉｓｅｘｃｉｔａｔｏｒｙｏｒｉｎｈｉｂｉｔｏｒｙ，ｉｎｃｒｅａｓｉｎｇｔｈｅｃｏｕｐｌｉｎｇｓｔｒｅｎｇｔｈｃａｎｐｒｏｍｏｔｅｔｈｅｓｐｉｋｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｏｆｎｅｕｒｏｎｓ，ｂｕｔｔｈｅｓｙｎｃｈｒｏｎｏｕｓｃｈａｎｇｅｏｆｅｘｃｉｔａｔｏｒｙｃｈｅｍｉｃａｌｓｙｎａｐｔｉｃｃｏｕｐｌｉｎｇｓｔａｔｅｉｓｍｏｒｅｏｂｖｉｏｕｓ．２．Ｔｈｅｃｈａｎｇｅｓｏｆ７ｂａｎｄｒｈｙｔｈｍａｎｄｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｔｒａｎｓｉｔｉｏｎｉｎｓｍａｌｌ－ｗｏｒｌｄｎｅｕｒａｌｎｅｔｗｏｒｋｓｗｅｒｅｆｕｒｔｈｅｒｉｎｖｅｓｔｉｇａｔｅｄ．ＵｓｉｎｇＩｚｈｉｋｅｖｉｃｈｎｅｕｒｏｎｓｗｉｔｈｄｉｆｆｅｒｅｎｔｆｉｒｉｎｇｐａｔｔｅｒｎｓ，ａｃｏｍｐｌｅｘｎｅｔｗｏｒｋｍｏｄｅｌｗｉｔｈａｓｍａｌｌ－ｗｏｒｌｄｎｅｔ？ｗｏｒｋｔｏｐｏｌｏｇｙｗａｓｃｏｎｓｔｒｕｃｔｅｄｔｏｓｉｍｕｌａｔｅｔｈｅｔｈｅｃｅｒｅｂｒａｌｃｏｒｔｅｘ．Ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｔｈｅｄｅｇｒｅｅｏｆｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｗａｓｓｉｇｎｉｆｉｃａｎｔｌｙｉｎｃｒｅａｓｅｄｗｉｔｈｔｈｅｉｎｃｒｅａｓｅｏｆｔｈｅｗｅｉｇｈｔｏｆｓｙｎａｐｔｉｃｃｏｎｎｅｃｔｉｏｎｓａｎｄｔｈｅｎｕｍｂｅｒｏｆｔｈｅｎｅａｒｅｓｔｎｅｉｇｈｂｏｒｓｏｆｅａｃｈｎｏｄｅ，ａｎｄｔｈｅｖａｒｉａｔｉｏｎｏｆｔｈｅ７ｂａｎｄｒｈｙｔｈｍｔｅｎｄｓｔｏｂｅｃｏｎ－ｓｉｓｔｅｎｔｗｉｔｈｔｈｅｄｅｇｒｅｅｏｆｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ．Ｓｅｃｏｎｄｌｙ，Ｃｈａｔｅｒｉｎｇｎｅｕｒｏｎｓｃａｎｉｎｄｕｃｅｔｈｅｎｅｔｗｏｒｋｔｏａｃｈｉｅｖｅｃｏｍｐｌｅｔｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｍｏｒｅｅｆｆｅｃｔｉｖｅｌｙ，ａｎｄｉｔｉｓｍｏｒｅｓｅｎｓｉｔｉｖｅｔｏｔｈｅｃｈａｎｇｅｏｆｃｏｎｔｒｏｌｐａｒａｍｅｔｅｒｓ．Ｆｉｎａｌｌｙ，ＲｅｇｕｌａｒＳｐｉｋ？ｉｎｇｎｅｕｒｏｎｓａｎｄＣｈａｔｔｅｒｉｎｇｎｅｕｒｏｎｓｗｅｒｅｕｓｅｄｔｏｊｏｉｎｔｌｙｓｉｍｕｌａｔｅｅｘｃｉｔａｔｏｒｙｎｅｕｒｏｎｓ，ｗｈｉｌｅＦａｓｔＳｐｉｋｉｎｇｎｅｕｒｏｎｓａｌｗａｙｓｍｉｍｉｃｉｎｈｉｂｉｔｏｒｙｎｅｕｒｏｎｓｉｎｔｈｅｎｅｕｒａｌｎｅｔｗｏｒｋ．Ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｔｈｅｎｅｕｒａｌｎｅｔｗｏｒｋｗｉｔｈｃｈａｔｔｅｒｉｎｇｎｅｕ？ｒｏｎｓｉｓｍｏｒｅｌｉｋｅｌｙｔｏａｃｈｉｅｖｅｃｏｍｐｌｅｔｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎｔｈａｎｔｈｏｓｅｗｉｔｈｒｅｇｕｌａｒｓｐｉｋｉｎｇｎｅｕｒｏｎｓ，ａｎｄｉｓｍｏｒｅｌｉｋｅｌｙｔｏｂｅａｆｆｅｃｔｅｄｂｙｐａｒａｍｅｔｅｒｃｈａｎｇｅｓ．３．Ａｃｏｍｐｌｅｘｎｅｔｗｏｒｋｃｏｎｓｉｓｔｉｎｇｏｆｅｘｃｉｔａｔｏｒｙ（Ｅ）ｐｙｒａｍｉｄａｌｎｅｕｒｏｎｓａｎｄｉｎｈｉｂｉｔｏｒｙ（Ｉ）ｉｎｔｅｍｅｕｒｏｎｓｗａｓｉｎｖｅｓｔｉｇａｔｅｄ，ａｎｄｔｈｅｅｆｆｅｃｔｓｏｆｔｈｅｉｎｔｅｒａｃｔｉｏｎｂｅｔｗｅｅｎｓｐｉｋｅ－ｔｉｍｉｎｇ－ｄｅｐｅｎｄｅｎｔｐｌａｓｔｉｃｉｔｙａｎｄｃｈｅｍｉｃａｌｓｙｎａｐｓｅｓｏｎｎｅｔｗｏｒｋｓｙｎｃｈｒｏｎｉｚａｔｉｏｎａｎｄｏｓｃｉｌｌａｔｉｏｎｂｅｈａｖｉｏｒｓｗｅｒｅｉｎｖｅｓｔｉｇａｔｅｄ．Ｔｏｅｘｐｌｏｒｅｔｈｅｅｆ？ｆｅｃｔｓｏｆｅＳＴＤＰａｎｄｉＳＴＤＰｏｎｔｈｅｔｈｅｓｙｎｃｈｒｏｎｏｕｓａｎｄｏｓｃｉｌｌａｔｏｒｙｂｅｈａｖｉｏｒｓｉｎｅｘｃｉｔａｔｏｒｙｉｎｈｉｂｉｔｏｒｙｂａｌａｎｃｅｄｃｏｒｔｉｃａｌｎｅｕｒａｌｎｅｔｗｏｒｋ，ｗｅｃｏｍｐａｒｅｄａｎｄａｎａｌｙｚｅｄｔｈｒｅｅｍｏｄｅｌｓ．ＴｈｅｆｉｒｓｔｉｓａｃｏｍｐｌｅｘｎｅｔｗｏｒｋｃｏｍｐｏｓｅｄｐｕｒｅｌｙｏｆｅｘｃｉｔａｔｏｒｙｐｙｒａｍｉｄａｌｎｅｕｒｏｎｓａｎｄｅＳＴＤＰｅｘｃｉｔａｔｏｒｙｓｙｎａｐｔｉｃｌｅａｒｎｉｎｇｍｅｃｈａ？ｎｉｓｍｓ，ａｎｄｔｈｅｓｅｃｏｎｄｉｓａｃｏｍｐｌｅｘｎｅｔｗｏｒｋｃｏｍｐｏｓｅｄｏｆｐｕｒｅｉｎｈｉｂｉｔｏｒｙｉｎ－ＩＶ

ｔｅｍｅｕｒｏｎｓａｎｄｉＳＴＤＰｉｎｈｉｂｉｔｏｒｙｓｙｎａｐｓｅｓ．Ｔｈｅｔｈｉｒｄｉｓａｎｅｘｃｉｔａｔｏｒｙｉｎｈｉｂｉｔｏｒｙｂａｌａｎｃｅｄｎｅｕｒａｌｎｅｔｗｏｒｋｃｏｍｂｉｎｅｄｗｉｔｈｍｉｘｅｄｓｙｎａｐｔｉｃｐｌａｓｔｉｃｉｔｙ．Ｂｙｃｈａｎｇ？ｉｎｇｔｈｅｎｕｍｂｅｒｏｆｉｎｈｉｂｉｔｏｒｙｉｎｔｅｍｅｕｒｏｎｓ，ｔｈｅｎｕｍｂｅｒｏｆｃｏｎｎｅｃｔｉｏｎｅｄｇｅｓｉｎｔｈｅｓｍａｌｌ－ｗｏｒｌｄｎｅｔｗｏｒｋｔｏｐｏｌｏｇｙａｎｄｔｈｅｃｏｕｐｌｉｎｇｓｔｒｅｎｇｔｈ，ｔｈｅｔｈｒｅｅｎｅｔｗｏｒｋｓｓｈｏｗｄｉｆｅｒｅｎｔｓｙｎｃｈｒｏｎｉｚａｔｉｏｎａｎｄｏｓｃｉｌｌａｔｉｏｎｂｅｈａｖｉｏｒｓ．Ｉｎａｄｄｉｔｉｏｎ，ｔｈｅｓｔｕｄｙｆｏｕｎｄｔｈａｔｅＳＴＤＰｃａｎｅｆｆｅｃｔｉｖｅｌｙｐｒｏｍｏｔｅｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ，ｗｈｉｌｅｉＳＴＤＰｈａｓｎｏｓｉｇｎｉｆｉｃａｎｔｅｆｆｅｃｔｏｎｓｙｎｃｈｒｏｎｉｚａｔｉｏｎ．Ｉｎｔｅｒｅｓｔｉｎｇｌｙ，ｅＳＴＤＰａｎｄｉＳＴＤＰａｃｔｔｏ？ｇｅｔｈｅｒａｎｄｒｅｓｔｒｉｃｔｅａｃｈｏｔｈｅｒｉｎｅｘｃｉｔａｔｏｒｙ－ｉｎｈｉｂｉｔｏｒｙｂａｌａｎｃｅｄｎｅｕｒａｌｎｅｔｗｏｒｋ，ａｎｄｐｌａｙａｎｉｍｐｏｒｔａｎｔｒｏｌｅｉｎｍａｉｎｔａｉｎｉｎｇｔｈｅｂａｌａｎｃｅａｎｄｓｔａｂｉｌｉｔｙｏｆｎｅｔｗｏｒｋ．ＫＥＹＷＯＲＤＳ：ＮｅｕｒａｌｎｅｔｗｏｒｋＳｍａｌｌ－ｗｏｒｌｄｎｅｔｗｏｒｋＳｙｎｃｈｒｏｎｉｚａｔｉｏｎＲｈｙｔｈｍＥｘｃｉｔａｔｏｒｙ－ＩｎｈｉｂｉｔｏｒｙＢａｌａｎｃｅｄｎｅｔｗｏｒｋＳｙｎａｐｔｉｃＰｌａｓｔｉｃｉｔｙＳｉｇ？ｎａｌＴｒａｎｓｍｉｓｓｉｏｎＭｅｃｈａｎｉｓｍｖ