Neuromolecular Computer Design through Adaptive Surface Engineering

[** 9704190] Conrad人工神经分子（ANM）架构是一种类似大脑的计算机设计，其中神经元单元的输入输出能力由内部动力学控制，用于融合空间和时间中的信号。这些内部（或神经元内）动力学是由分子过程驱动的，被认为在真实的神经元中起作用。全局架构的模式处理和控制能力利用了神经元级和网络级的动态。进化算法将这些技能编排成适合连贯感知-行动任务的分组，包括涉及复杂动作序列的任务。该系统被应用于迷宫环境下的汉字识别和识别/效应器控制行为。这个项目的目标是发展一种设计网络的方法，以提高进化计算作为一种适应技术的有效性。采用AM架构作为测试平台。为此目的，开发了四个具有不同类型动力学的子模型。第一种是抽象的（双动态）网络模型，它由受弱集体交互影响的模式识别组件组成。第二种是细胞基质神经元，是一种软化的细胞自动机。第三种是由神经元细胞骨架驱动的耦合振荡器模型。第四是一个通用的模拟系统，可以用来模拟神经元细胞骨架的驱动。第四个是一个通用的模拟系统，可以用来模拟各种各样的细胞内动态过程，包括动力学过程和结构过程的共同作用。这些不同的子模型可以单独研究，也可以嵌入到ANM系统的神经单元中。这些模型的对比实验研究补充了理论分析，并为与各种神经结构相关的自适应表面工程提供了指导。* * *

*** 9704190 Conrad Artificial neuromolecular (ANM) architectures are brain-like computer designs in which the input-output capabilities of the neuronal units are controlled by internal dynamics that serve to fuse signals in space and time. These internal (or intra-neuronal) dynamics are motivated by molecular process believed to be operative in real neurons. The pattern processing and control capabilities of the global architecture this draw on neuronal as well as network level dynamics. Evolutionary algorithms orchestrate this repertoire into groupings suitable for coherent perception-action tasks, including tasks involving complex sequences of actions. The system is being applied to Chinese character differentiation and recognition/effector control behavior in a maze-like environment. The objective of this project is to develop a methodology for designing networks to increase the effectiveness of evolutionary computing as an adaptation technique. The AM architecture is use as the testbed. Four submodels with different types of dynamics have bee developed for this purpose. The first is an abstract (dual dynamics), network model that consist of pattern recognizing components subject to weak collective interactions. The second, the cytomatrix neuron, is a softened cellular automaton. the third is a coupled oscillator model motivated by the neuron cytoskeleton. the fourth is a general simulation system that can be used to model motivated by the neuronal cytoskeleton. The fourth is a general simulation system that can be used to model a wide variety of intracellular dynamic processes involving the co-action of kinetic and structural processes. These different submodels con be studied in their own right and also can be embedded in the neural units of ANM system. The comparative experimental study of these models complement theoretical analysis and provided guidelines for adaptive surface engineering pertinent to a wide variety of neural architectures. ***