Neuromolecular Computer Design through Adaptive Surface Engineering

* 9704190 Conrad人工神经分子（ANM）架构是类脑计算机设计，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）动力学是由被认为在真实的神经元中起作用的分子过程激发的。 全局架构的模式处理和控制能力，这借鉴了神经元以及网络级动态。 进化算法将这些指令编排成适合于连贯的感知-动作任务的分组，包括涉及复杂动作序列的任务。 该系统已被应用于迷宫环境中的汉字识别和识别/效应器控制行为。这个项目的目标是开发一种设计网络的方法，以提高进化计算作为适应技术的有效性。 AM架构被用作测试床。 四个子模型与不同类型的动力学已被开发用于此目的。 第一个是一个抽象的（双动力学），网络模型，包括模式识别组件受到弱集体相互作用。 第二种是细胞基质神经元，它是一种软化的细胞自动机。 第三种是由神经元细胞骨架激励的耦合振子模型。 第四个是一个通用的模拟系统，可以用来模拟由神经元细胞骨架驱动的。 第四个是一个通用的模拟系统，可用于模拟各种各样的细胞内动态过程，涉及动力学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系统的神经元中。 这些模型的比较实验研究补充了理论分析，并为各种神经结构的自适应表面工程提供了指导方针。***

*** 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. ***