989 resultados para Optimal spectral partition


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A new, and very simple spectrometer based on birefringent fiber is described. A resolution of 0.02 angstrom has been achieved, and the system has been used to measure diode laser chirp. A length of 10km of fiber would be sufficient to resolve single mode line widths.

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We report a novel OTDM/WDM source based on spectral slicing of a passively mode-locked Cr4: YAG femtosecond pulse source. Total capacities up to 682Gbit/s and 1.36bit/s with spectral efficiencies of 0.2b/s/Hz and 0.4b/s/Hz have been achieved. © 2003 Optical Society of America.

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The trajectory of the somatic membrane potential of a cortical neuron exactly reflects the computations performed on its afferent inputs. However, the spikes of such a neuron are a very low-dimensional and discrete projection of this continually evolving signal. We explored the possibility that the neuron's efferent synapses perform the critical computational step of estimating the membrane potential trajectory from the spikes. We found that short-term changes in synaptic efficacy can be interpreted as implementing an optimal estimator of this trajectory. Short-term depression arose when presynaptic spiking was sufficiently intense as to reduce the uncertainty associated with the estimate; short-term facilitation reflected structural features of the statistics of the presynaptic neuron such as up and down states. Our analysis provides a unifying account of a powerful, but puzzling, form of plasticity.

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Most behavioral tasks have time constraints for successful completion, such as catching a ball in flight. Many of these tasks require trading off the time allocated to perception and action, especially when only one of the two is possible at any time. In general, the longer we perceive, the smaller the uncertainty in perceptual estimates. However, a longer perception phase leaves less time for action, which results in less precise movements. Here we examine subjects catching a virtual ball. Critically, as soon as subjects began to move, the ball became invisible. We study how subjects trade-off sensory and movement uncertainty by deciding when to initiate their actions. We formulate this task in a probabilistic framework and show that subjects' decisions when to start moving are statistically near optimal given their individual sensory and motor uncertainties. Moreover, we accurately predict individual subject's task performance. Thus we show that subjects in a natural task are quantitatively aware of how sensory and motor variability depend on time and act so as to minimize overall task variability.