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Computer networking topological and parametric representations for functional neural networks

机译:功能神经网络的计算机网络拓扑和参数表示

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The correlated biochemistry and electrical-engineering-based development of the entropy theory is formalized for the parametrization of path information, i.e. storage measures. Similarly, the computer and neural network path delays are developed. Both path parameters are illustrated for the computer networking topological representation of two neural function organs and their interconnection, i.e. the star, the hippocampus computer network representation, and the wheel with a hub, the cerebellum computer network representation. The information measures illustrate the topologically motivated data-compression and connectivity differences. The following computer networking issues are realized in the topological neural network conversion to a functional computer network derivation of path delay and storage parameters: load, real-time requests for various topological structures, concurrency in centralized and distributed networks, deadlock and livelock control, and contention resolution. Category theory is utilized as the mathematical formulation for the definition of simulation in this software theory.
机译:熵理论基于生物化学和电气工程的相关发展已被形式化,用于路径信息(即存储措施)的参数化。类似地,开发了计算机和神经网络路径延迟。示出了两个路径参数,用于两个神经功能器官及其互连的计算机网络拓扑表示,即星形,海马计算机网络表示和带有轮毂的轮子,小脑计算机网络表示。这些信息度量说明了拓扑动机上的数据压缩和连接性差异。在将拓扑神经网络转换为功能性计算机网络时,会遇到以下计算机网络问题:路径延迟和存储参数的推导:负载,对各种拓扑结构的实时请求,集中式和分布式网络中的并发,死锁和活锁控制,以及争用解决。类别理论被用作该软件理论中模拟定义的数学公式。

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