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.

Low short term timing jitter in an integrated monolithic extended cavity semiconductor mode-locked laser

Jitter measurements were performed on a monolithically integrated active/passive cavity multiple quantum well laser, actively mode-locked at 10 GHz via modulation of an absorber section. Sub-10 ps pulses were produced upon optimization of the drive conditions to the gain, distributed Bragg reflector, and absorber sections. A model was also developed using travelling wave rate equations. Simulation results suggest that spontaneous emission is the dominant cause of jitter, with carrier dynamics having a time constant of the order of 1 ns.

Short-term in vitro responses of human peripheral blood monocytes to ferritic stainless steel fiber networks.

Beneficial effects on bone-implant bonding may accrue from ferromagnetic fiber networks on implants which can deform in vivo inducing controlled levels of mechanical strain directly in growing bone. This approach requires ferromagnetic fibers that can be implanted in vivo without stimulating undue inflammatory cell responses or cytotoxicity. This study examines the short-term in vitro responses, including attachment, viability, and inflammatory stimulation, of human peripheral blood monocytes to 444 ferritic stainless steel fiber networks. Two types of 444 networks, differing in fiber cross section and thus surface area, were considered alongside austenitic stainless steel fiber networks, made of 316L, a widely established implant material. Similar high percent seeding efficiencies were measured by CyQuant® on all fiber networks after 48 h of cell culture. Extensive cell attachment was confirmed by fluorescence and scanning electron microscopy, which showed round monocytes attached at various depths into the fiber networks. Medium concentrations of lactate dehydrogenase (LDH) and tumor necrosis factor alpha (TNF-α) were determined as indicators of viability and inflammatory responses, respectively. Percent LDH concentrations were similar for both 444 fiber networks at all time points, whereas significantly lower than those of 316L control networks at 24 h. All networks elicited low-level secretions of TNF-α, which were significantly lower than that of the positive control wells containing zymosan. Collectively, the results indicate that 444 networks produce comparable responses to medical implant grade 316L networks and are able to support human peripheral blood monocytes in short-term in vitro cultures without inducing significant inflammatory or cytotoxic effects.

Variability in encoding precision accounts for visual short-term memory limitations.

It is commonly believed that visual short-term memory (VSTM) consists of a fixed number of "slots" in which items can be stored. An alternative theory in which memory resource is a continuous quantity distributed over all items seems to be refuted by the appearance of guessing in human responses. Here, we introduce a model in which resource is not only continuous but also variable across items and trials, causing random fluctuations in encoding precision. We tested this model against previous models using two VSTM paradigms and two feature dimensions. Our model accurately accounts for all aspects of the data, including apparent guessing, and outperforms slot models in formal model comparison. At the neural level, variability in precision might correspond to variability in neural population gain and doubly stochastic stimulus representation. Our results suggest that VSTM resource is continuous and variable rather than discrete and fixed and might explain why subjective experience of VSTM is not all or none.

Short-term cytotoxic and inflammatory responses of human monocytes to stainless steel fibre networks

The aim of the current work was to examine the human monocyte response to 444 ferritic stainless steel fibre networks. 316L austenitic fibre networks, of the same fibre volume fraction, were used as control surfaces. Fluorescence and scanning electron microscopies suggest that the cells exhibited a good degree of attachment and penetration throughout both networks. Lactate Dehydrogenase (LDH) and TNF-α releases were used as indicators of cytotoxicity and inflammatory responses respectively. LDH release indicated similar levels of monocyte viability when in contact with the 444 and 316L fibre networks. Both networks elicited a low level secretion of TNF-α, which was significantly lower than that of the positive control wells containing zymosan. Collectively, the results suggest that 444 ferritic and 316L austenitic networks induced similar cytotoxic and inflammatory responses from human monocytes. © 2012 Materials Research Society.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.