Study On Multistability Based On Synaptic Plasticity

In various brain regions,the functional neural state is highly dynamic.As a result,the slight changes in the structure of complex brain neural networks lead to rapid responses in neuronal firing patterns,overall activity,and synchrony.It is well-known that the brain has inherent instability and multistability.Multistability is a phenomenon in which the activity of neurons changes with time and can switch easily between each state,which is closely related to various brain functions.The disorder of multistability induces the dysfunction of neural circuits and behaviors,such as Parkinson’s syndrome,epilepsy,and other diseases.Previous studies on multistability mainly focused on the effects of network structure and synaptic coupling on multistable dynamics.Several experiments later showed that for a neuronal network to function properly,its overall activity level must remain within the physiological range between complete silence and hyper excitation.The balance between excitability and inhibition of the network is a basic element of neuronal processing.Its role in the generation and maintenance of multistability is one of the hot topics in brain science.Previous studies have shown that the excitatory and inhibitory coupling level determines the balance of neural networks.At the same time,recent experiments have found that synaptic plasticity can break and modulate the network balance.Synaptic plasticity refers to the fact that changes in experience and activity can alter synapses’ relative strength,structure,and function.Brain function arises from dynamic stability,and stability stems from a tight emotional balance of excitatory and inhibitory activity,so plasticity mechanisms must exist to adapt the balance between excitatory and inhibitory activity to rapidly changing activity in the network.But so far,the excitation rule of the multistable nervous system and its internal mechanism are still unclear and need further study.Based on the above background,a neural network model of excitatory and inhibitory balance is constructed in this paper based on the specific synaptic coupling mode and the dynamic properties of neurons themselves.The Wang-Buzsaki model,a model of periodic oscillations,was chosen for the neurons model in the network.The network structure includes an excitatory and an inhibitory neuronal population.The excitatory neuron population is coupled by mixed synapses and distributed in a small-world topology.Electrical synapses connect the adjacent excitatory neurons,and the internal neurons are also associated with each other by chemical synapses.There are all-to-all connections via chemical synapses between neurons within the inhibitory neuron population and between excitatory and inhibitory neuron populations.The self-excitatory coupling with short-term synaptic plasticity and recurrent inhibitory coupling with short-term synaptic plasticity are then considered.The plasticity rule in the Tsodyks-Markram model is adopted.This study mainly focuses on the effect of mixed synapses and short-term synaptic plasticity on the dynamics of the excitatory neuron population.Based on this,my research is carried out from three aspects:1.In the absence of synaptic plasticity,the effect of hybrid synapses on the multistable neural network was explored based on the excitatory-inhibitory balanced neural network model.Increasing the weight of electrical synaptic coupling between excitatory cells has been found to promote the synchronization of the firing of the whole excitatory neuron population.In the presence of electrical synapses between excitatory neurons,changes in the coupling weights of chemical synapses can induce a variety of new and exciting firing patterns,such as traveling waves,synchronous oscillations,chimera-like,and multistable states.Moreover,these observed firing patterns cannot be motivated by single synaptic connections.Then we further investigated the effect of inhibitory synaptic connectivity on neuronal activity.We found that when there is electrical synaptic coupling between excitatory neurons at weak excitability levels,changes in the weight of inhibitory coupling can induce firing patterns of multiple ripple events with different time scales.At a strong group of excitability,changes in the inhibitory coupling weight can cause the neural network to generate three basic firing patterns: traveling waves,multistable state,and subthreshold oscillation.These results indicate that changes in the strength of the inhibitory coupling can produce nonlinear effects,which can cause the network to create different firing states.In conclusion,in excitatory-inhibitory balance neural networks,the interaction of chemical and electrical synapses can cause the network to produce different spatiotemporal firing patterns.2.Explore the effect of short-term synaptic plasticity on self-excitatory connections in multistable neural networks.In the constructed balanced neural network,self-excitatory connectivity introduces the regulatory effect of short-term synaptic plasticity.We found that the network can maintain and stabilize the dynamic behavior so that it does not change with the change of excitability strength.In addition,for a given level of excitability,we found that recurrent inhibitory connectivity is a mechanism to regulate the firing state of neurons in a plastic network.Changes in inhibitory coupling strength can enable the network to generate rich firing states.Finally,we compared the situation of short-term facilitation and short-term depression and found that the results obtained were robust.3.Explore the effect of short-term synaptic plasticity on recurrent inhibitory in multistable neural networks.In the constructed balanced neural network,recurrent inhibitory connectivity introduces the regulatory effect of short-term synaptic plasticity.We found that electrical synaptic coupling strength and short-term synaptic plasticity are essential factors regulating neural network activity.Short-term synaptic plasticity can reduce the diversity of network activities,and electrical synaptic coupling plays a critical role in stabilizing neural firing.In addition,we found an excitatory and inhibitory balance curve in the excitatory and inhibitory parameter space,and its position would be affected by the strength of electrical synaptic coupling.