The Effect of SiO2, TiO2, and B2O3 on the Crystallization of Glass-Ceramics Glaze Properties

The feasibility of developing glass-ceramic glaze in the system KNaO-CaO-MgO-ZnO with a variation in the composition of SiO2, TiO2, and B2O3 was studied. The SiO2, TiO2, and B2O3 were varied in the amount of 2.25-1.50, 0.001-0.10, and 0-0.1 molar equivalents respectively. The samples were one fired at 1180°C or double fired by reheat at the crystallization temperature for 10 minutes. The gloss, sintering behavior, phase, microstucture, and hardness, and were examined by glossmeter, side-view hot stage microscope, X-ray diffraction, SEM, and Vickers hardness respectively. The results showed the importance effect of SiO2, TiO2, and B2O3 on the glaze crystallization ability and its properties. At the fix value of Al2O3 at 0.24 molar equivalents and with the 0.001-0.10 molar equivalents of TiO2, lower the SiO2 content to 1.50 molar equivalents increased the glaze crystallization potential. An increase in the B2O3 to 0.1 molar equivalents suppressed the potential of glaze crystallization. The phases of samples were amorphous or composed of silicon dioxide and diopside as the main phases depending on the glaze composition and the firing history. In this study, the glaze appearances transparent to opaque and varied from gloss to matte with the specular gloss values between 23-100 GU. All samples appeared to have high Vickers hardness value in the range of 553-644. The crystallization decreased the gloss but increased the hardness value for the 2.25 molar equivalents SiO2 glaze. Finally, a composition with high hardness and high gloss was identified and its properties was also presented. These results suggested the limitation and the potential for applying this glass-ceramic glaze system to industry applications.

Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach

Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

Dynamics of Deterministic and Stochastic Paired Excitatory—Inhibitory Delayed Feedback

We examine the effects of paired delayed excitatory and inhibitory feedback on a single integrate—and—fire neuron with reversal potentials embedded within a feedback network. These effects are studied using bifurcation theory and numerical analysis. The feedback occurs through modulation of the excitatory and inhibitory conductances by the previous firing history of the neuron; as a consequence, the feedback also modifies the membrane time constant. Such paired feedback is ubiquitous in the nervous system. We assume that the feedback dynamics are slower than the membrane time constant, which leads to a rate model formulation. Our article provides an extensive analysis of the possible dynamical behaviors of such simple yet realistic neural loops as a function of the balance between positive and negative feedback, with and without noise, and offers insight into the potential behaviors such loops can exhibit in response to time-varying external inputs. With excitatory feedback, the system can be quiescent, can be periodically firing, or can exhibit bistability between these two states. With inhibitory feedback, quiescence, oscillatory firing rates, and bistability between constant and oscillatory firing-rate solutions are possible. The general case of paired feedback exhibits a blend of the behaviors seen in the extreme cases and can produce chaotic firing. We further derive a condition for a dynamically balanced paired feedback in which there is neither bistability nor oscillations. We also show how a biophysically plausible smoothing of the firing function by noise can modify the existence and stability of fixed points and oscillations of the system. We take advantage in our mathematical analysis of the existence of an invariant manifold, which reduces the dimensionality of the dynamics, and prove the stability of this manifold. The novel computational challenges involved in analyzing such dynamics with and without noise are also described. Our results demonstrate that a paired delayed feedback loop can act as a sophisticated computational unit, capable of switching between a variety of behaviors depending on the input current, the relative strengths and asymmetry of the two parallel feedback pathways, and the delay distributions and noise level.

Modulation of pacemaker activity by IPSP and brief length perturbations in the crayfish stretch receptor

We have studied the influences of brief stretches and releases and of inhibitory postsynaptic potentials (IPSPs) on pacemaker activity of the crayfish stretch receptor (RM1). Stimuli shift or reset the ongoing rhythm. Resettings were different if evaluated in interspike intervals containing perturbations, or in succeeding ones, and are referred to as early and late, respectively. Early resetting revealed that stretches and releases or IPSPs advance and delay, respectively, the next spike. With small stretches and releases or IPSPs, effects depend on the timing of the perturbation relative to the previous spike or phase, but above a characteristic mechanical perturbation amplitude the next spike fires at a fixed latency, invariant with the phase. Of particular interest was the finding that during late resetting the first successive intervals following stretches and releases or IPSPs were longer and shorter, respectively, than the period. This led, in approximately 50% of the cases, to a gradual recovery of the original pacemaker beat in the sense that spikes fire timed as if the early rhythm shift had not occurred. In conclusion, the recent firing history is essential in determining the RM1's response. The receptor's sensitivity is a complex nonlinear and periodic function of the pacemaker activity, and the response is due to interactions between pacemaker- and perturbation-induced transmembrane ionic currents. Although several alternative mechanisms may underly beat recovery, the results suggest that at least two coupled oscillators, one perturbable and the other not, provide a better explanation than a single oscillator. The physiological significance of resettings is unknown, but the early rhythm shift may synchronize RM1s in several segments when the animal's tail is moved, and conversely recovery would reduce synchrony, with obvious influences on shared postsynaptic neurons.