Synthesis of an optical neuromorphic structure with differentiated artificial neurons for information flow distribution

IF 0.4 Q4 MATHEMATICS, APPLIED
Yury N. Lavrenkov
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引用次数: 0

Abstract

The work presents analysis of possible application of self-generating neural networks, which can independently generate a topological map of neuron connections while modelling biological neurogenesis, in multi-threaded information communication systems. A basic optical neural network cell is designed on the basis of the applied layered composition performing data processing. A map of neuron connections represents not an ordered structure providing a regular graph for exchange of information between neurons, but a set of cognitive reserve represented as an unconnected set of neuromorphic cells. Modelling of neuron death (apoptosis) and creation of dendrite-axon connections makes it possible to implement a stepwise neural network growth algorithm. Despite challenges in implementing this process, creating a growing network in an optical neural network framework solves the problem of initial forming of the neural network architecture, which greatly simplifies the learning process. Neural network cells used with the network growth algorithm resulted in neural network structures that use internal self-sustaining rhythmic activity to process information. This activity is a result of spontaneously formed closed neural circuits with common neurons among neuronal cells. Such organisation of recirculation memory leads to solutions with reference to such intra-network activity. As a result, response of the network is determined not only by stimuli, but also by the internal state of the network and its rhythmic activity. Network functioning is affected by internal rhythms, which depend on the information passing through the neuron clusters, which results in formation of a specific rhythmic memory. This can be used for tasks that require solutions to be worked out based on certain parameters, but they shall be unreproducible when the network is repeatedly stimulated by the same influences. Such tasks include ensuring information transmission security when using some set of carriers. The task of determining a number of frequencies and their frequency plan depends on external factors. To exclude possible repeating generation of the same carrier allocation, it is necessary to use networks of the configuration under consideration that can influence generation of solutions through the gathered experience.
合成具有分化人工神经元的光学神经形态结构用于信息流分布
这项工作分析了自生成神经网络在多线程信息通信系统中的可能应用,该网络可以在模拟生物神经发生时独立生成神经元连接的拓扑图。在应用分层组合进行数据处理的基础上,设计了一个基本的光学神经网络单元。神经元连接图不是为神经元之间的信息交换提供规则图的有序结构,而是一组认知储备,表示为一组未连接的神经形态细胞。神经元死亡(凋亡)的建模和树突-轴突连接的建立使得实现逐步神经网络增长算法成为可能。尽管在实现这一过程中存在挑战,但在光神经网络框架中创建生长网络解决了神经网络架构的初始形成问题,从而大大简化了学习过程。与网络增长算法一起使用的神经网络细胞产生了利用内部自我维持的节律活动来处理信息的神经网络结构。这种活动是自发形成的封闭神经回路的结果,神经元细胞之间有共同的神经元。这种循环存储器的组织导致参考这种网络内活动的解决方案。因此,神经网络的反应不仅取决于刺激,还取决于神经网络的内部状态及其节律性活动。网络功能受内部节律的影响,内部节律依赖于通过神经元簇的信息,从而形成特定的节律性记忆。这可以用于需要根据某些参数计算出解决方案的任务,但当网络受到相同影响的反复刺激时,它们是不可复制的。这些任务包括在使用某些载波时确保信息传输的安全性。确定若干频率及其频率计划的任务取决于外部因素。为了排除可能的重复生成相同的载波分配,有必要使用所考虑的配置的网络,该网络可以通过收集的经验影响解决方案的生成。
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CiteScore
0.70
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