Demonstration of a compact InGaAsP active crosspoint switch operating at 1.5 μm using novel reflective y-coupler components

An extremely compact active optoelectronic crosspoint switch, having overall dimensions of 400 μm×200 μm, is reported. The device provides unity facet-to-facet gain for both bar and cross state operation for TE or TM input signals.

Experimental demonstration of femtosecond switching of a fully packaged all-optical switch

We experimentally demonstrate femtosecond switching of a fully packaged hybrid-integrated Mach-Zehnder switch. A record switching window of 620fs at fult-width-half-maximum is achieved. © 2004 Optical Society of America.

A novel scalable photonic analog switch architecture based on semiconductor optical amplifiers

We propose a novel semiconductor optical amplifier (SOA) based switch architecture for analog applications. Proof-of-principle experiments show that the system is very linear with an SFDR of approximately 100dB·Hz 2/3 for a switching time of 50μs. The port number of this switch is scalable and can be expanded to 80 × 80.

Low penalty cascaded operation of a monolithically integrated Quantum Dot 1×8 port optical switch

A novel Quantum Dot monolithically integrated 1×8 switch is shown to provide robust routing of data at 10Gb/s modulation rates. Two cascaded switches providing, 1×64 functionality, operate with a power penalty of only 0.9dB. © VDE VERLAG GMBH.

Large port count high-speed optical switch fabric for use within datacenters [Invited]

This paper reviews advances in the technology of integrated semiconductor optical amplifier based photonic switch fabrics, with particular emphasis on their suitability for high performance network switches for use within a datacenter. The key requirements for large port count optical switch fabrics are addressed noting the need for switches with substantial port counts. The design options for a 16×16 port photonic switch fabric architecture are discussed and the choice of a Clos-tree design is described. The control strategy, based on arbitration and scheduling, for an integrated switch fabric is explained. The detailed design and fabrication of the switch is followed by experimental characterization, showing net optical gain and operation at 10 Gb/s with bit error rates lower than 10-9. Finally improvements to the switch are suggested, which should result in 100 Gb/s per port operation at energy efficiencies of 3 pJ/bit. © 2011 Optical Society of America.

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials.

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.

Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task.

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.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.