Many cyanobacteria exhibit surface motility powered by type 4 pili (T4P). In the model filamentous cyanobacterium Nostoc punctiforme, the T4P systems are arrayed in static, bipolar rings in each cell. The chemotaxis-like Hmp system is essential for motility and the coordinated polar accumulation of PilA on cells in motile filaments, while the Ptx system controls positive phototaxis. Using transposon mutagenesis, a gene, designated hmpF, was identified as involved in motility. Synteny among filamentous cyanobacteria and the similar expression patterns for hmpF and hmpD imply that HmpF is part of the Hmp system. Deletion of hmpF produced a phenotype distinct from other hmp genes, but indistinguishable from pilB or pilQ. Both an HmpF-GFPuv fusion protein, and PilA, as assessed by in situ immunofluorescence, displayed coordinated, unipolar localization at the leading pole of each cell. Reversals were modulated by changes in light intensity and preceded by the migration of HmpF-GFPuv to the lagging cell poles. These results are consistent with a model where direct interaction between HmpF and the T4P system activates pilus extension, the Hmp system facilitates coordinated polarity of HmpF to establish motility, and the Ptx system modulates HmpF localization to initiate reversals in response to changes in light intensity.

Abstract-Recently, there has been strong interest in large-scale simulations of biological spiking neural networks (SNN) to model the human brain mechanisms and capture its inference capabilities. Among various spiking neuron models, the Hodgkin-Huxley model is the oldest and most compute intensive, whereas the more recent Izhikevich model is very compute efficient. Some of the recent multi-core processors and accelerators including Graphical Processing Units, IBM's Cell Broadband Engine, AMD Opteron, and Intel Xeon can take advantage of task and thread level parallelism, making them good candidates for large-scale SNN simulations. In this paper we implement and analyze two character recognition networks based on these spiking neuron models. We investigate the performance improvement and optimization techniques for SNNs on these accelerators over an equivalent software implementation on a 2.66 GHz Intel Core 2 Quad. We report significant speedups of the two SNNs on these architectures. It has been observed that given proper application of optimization techniques, the commodity X86 processors are viable options for those applications that require a nominal amount of flops/byte. But for applications with a significant number of flops/byte, specialized architectures such as GPUs and cell processors can provide better performance. Our results show that a proper match of architecture with algorithm complexity provides the best performance.

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