{"title":"Classical Soliton Theory for Studying the Dynamics and Evolution of in Network","authors":"S. Belyakin","doi":"10.31579/2690-1919/186","DOIUrl":null,"url":null,"abstract":"This paper presents the dynamic model ofthe soliton. Based on this model, it is supposed to study the state of the network. The term neural networks refersto the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons.","PeriodicalId":93114,"journal":{"name":"Journal of clinical research and reports","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of clinical research and reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31579/2690-1919/186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents the dynamic model ofthe soliton. Based on this model, it is supposed to study the state of the network. The term neural networks refersto the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons.