Pattern Turnover within Synaptically Perturbed Neural Systems

AbstractA critical level of synaptic perturbation within a trained, artificial neural system induces the nucleation of novel activation patterns, many of which could qualify as viable ideas or action plans. In building massively parallel connectionist architectures requiring myriad, coupled neural modules driven to ideate in this manner, the need has arisen to shift the attention of computational critics to only those portions of the neural “real estate” generating sufficiently novel activation patterns. The search for a suitable affordance to guide such attention has revealed that the rhythm of pattern generation by synaptically perturbed neural nets is a quantitative indicator of the novelty of their conceptual output, that cadence in turn characterized by a frequency and a corresponding temporal clustering that is discernible through fractal dimension. Anticipating that synaptic fluctuations are tantamount in effect to volume neurotransmitter release within cortex, a novel theory of both cognition and consciousness arises that is reliant upon the rate of transitions within cortical activation topologies

Demonstration of Self-Training Autonomous Neural Networks in Space Vehicle Docking Simulations

Neural Networks have been under examination for decades in many areas of research, with varying degrees of success and acceptance. Key goals of computer learning, rapid problem solution, and automatic adaptation have been elusive at best. This paper summarizes efforts at NASA's Marshall Space Flight Center harnessing such technology to autonomous space vehicle docking for the purpose of evaluating applicability to future missions