Signal Transmission With Synaptic Plasticity In Complex Neural Systems

The transmission of information 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 that electrical synaptic coupling can enhance the synchrony of neurons.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 ? band rhythm and synchronization transition in small-world neural networks were further investigated.Using Izhikevich neurons with different firing patterns,a complex network model with a small-world net-work topology was constructed to simulate the the 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 ? 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 the the 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-terneurons and iSTDP inhibitory synapses.The third is an excitatory inhibitory balanced neural network combined with mixed synaptic plasticity.By chang-ing the number of inhibitory interneurons,the number of connection edges in the small-world network topology and the coupling strength,the three networks show different synchronization and oscillation behaviors.In addition,the study found that eSTDP can effectively promote synchronization,while iSTDP has no significant effect on synchronization.Interestingly,eSTDP and iSTDP act to-gether and restrict each other in excitatory-inhibitory balanced neural network,and play an important role in maintaining the balance and stability of network.