Pattern Processing and Control with a Virtual NeuromolecularComputer Architecture

9409780康拉德一个多层次的神经分子计算架构已经开发出来，提供了一个丰富的平台，进化学习。 该体系结构包括一个网络的神经元样的模块与内部动力学建模的细胞自动机。 动力学反映了被认为在真实的神经元中起作用的分子过程，特别是与第二信使信号和细胞膜相互作用有关的过程。 我们的目标是创建一个专用的动态模式处理器，通过和进化搜索算法，然后使用内存操作算法来选择处理器的组合，从剧目，能够执行连贯的模式识别/神经控制任务。 该系统，目前实施，由两层的细胞骨架神经元和两层的记忆访问神经元（称为参考神经元）划分成一个集合的功能相当的子。 进化学习可以发生在神经元内水平，通过负责整合信号的空间和时间的细胞骨架结构的变化，或通过代表readin或readout蛋白的元件位置的变化。 编排神经元处理器的指令集的记忆操作算法也使用进化搜索程序，并且也非常适合于在联想模式下操作。 该集成系统有效地利用了不同层次的处理和学习之间的协同作用，以获得在迷宫般的环境中寻路的能力。 研究的近期目标包括系统性能的全面实验表征，拓宽神经元的动态能力，研究系统结构与自适应表面的“进化友好性”之间的关系，结合进化学习利用联想记忆能力，以及研究系统在各种应用领域中的应用。 长期目标包括实现开放式的进化学习，移植进化的神经元与有用的模式处理能力，特殊的硅硬件，使用该系统作为新兴的分子电子技术的架构范例，并使用该系统作为一种工具，以获得更清楚地了解脑功能的神经元内机制的作用。 ***

9409780 Conrad A multilevel neuromolecular computing architecture has been developed that provides a rich platform for evolutionary learning. The architecture comprises a network of neuron-like modules with internal dynamics modeled by cellular automata. The dynamics reflect molecular processes believed to be operative in real neurons, in particular processes connected with second messenger signals and cytoskeleton-membrane interactions. The objective is to create a repertoire of special purpose dynamic pattern processors through and evolutionary search algorithm and then to use memory manipulation algorithms to select combinations of processors from the repertoire that are capable of performing coherent pattern recognition/neurocontrol tasks. The system, as presently implemented, consists of two layers of cytoskeletal neurons and two layers of memory access neurons (called reference neurons) divided into a collection of functionally comparable sublets. Evolutionary learning can occur at the intraneuronal level through variations in the cytoskeletal structures responsible for the integration of signals in space and time or through variations in the location of elements that represent readin or readout proteins. The memory manipulation algorithms that orchestrate the repertoire of neuronal processors also use evolutionary search procedures, and are well suited for operating in an associative mode as well. The integrated system effectively employs synergies among the different levels of processing and learning to acquire pathfinding capabilities in a maze-like environment. Immediate objectives of the research include full experimental characterization of the performance capabilities of the system, widening the dynamic capabilities of neurons, studying the relation between the structure of the system and the "evolution friendliness" of the adaptive surface, utilization of the associative memory capability in combination with evolutionary learning, and study of the system in a variety of a pplication domains. Long term goals include the achievement of open ended evolutionary learning, porting evolved neurons with useful pattern processing capabilities to special silicon hardware, use of the system as an architectural paradigm for emerging molecular electronic technologies, and use of the system as a vehicle for obtaining a clearer understanding of the role of intraneuronal mechanisms in brain function. ***