924 resultados para A.S. degree
Resumo:
Prior synaptic or cellular activity influences degree or threshold for subsequent induction of synaptic plasticity, a process known as metaplasticity. Thus, the continual synaptic activity, spontaneous miniature excitatory synaptic current (mEPSC) may correlate to the induction of long-teen depression (LTD). Here, we recorded whole-cell EPSC and mEPSC alternately in the Schaffer-CA1 synapses in brain slice of young rats, and found that this recording configuration affected neither EPSC nor mEPSC. Low frequency stimulation (LFS) induced variable magnitudes of LTD. Remarkably, larger magnitudes of LTD were significantly correlated to smaller amplitude/lower frequency of the basal mEPSC. Furthermore, under the conditions reduced amplitude/frequency of the basal mEPSC by exposure to behavioral stress immediately before slice preparation or low concentration of calcium in bath solution, the magnitudes of LTD were still inversely correlated to mEPSC amplitude/frequency. These new findings suggest that spontaneous mEPSC may reflect functional and/or structural aspects of the synapses, the synaptic history ongoing metaplasticity. (C) 2005 Elsevier B.V. All rights reserved.
Resumo:
Photon quantum statistics of light can be shown by the high-order coherence. The fourth-order coherences of various quantum states including Pock states, coherent states, thermal states and squeezed vacuum states are investigated based on a double Banbury Brown Twiss (HBT) scheme. The analytical results are obtained by taking the overall efficiency and background into account.
Resumo:
We study the entanglement degree of electron pairs emitted from an s-wave Superconductor, which Couples to two normal leads via a single-level quantum dot. Within the framework of scattering matrix theory. the concurrence is used to quantify the entanglement. And the result shows that the entanglement degree is generally influenced by the initial separation of the two electrons in a Cooper pair and the normal transmission eigenvalues T-1, T-2. But it is only determined by the eigenvalues in the tunnelling limit, T-1. T-2 << 1, what is more. it is measurable. (C) 2008 Elsevier B.V. All rights reserved.
Resumo:
The strain state of 570nm AlXGa1-xN layers grown on 600nm GaN template by metal organic chemical vapor deposition was studied using Rutherford backscattering (RBS)/channeling and triple-axis X-ray diffraction measurements. The results showed that the degree of relaxation (R) of AlxGa1-xN layers increased almost linearly when x less than or equal to 0.42 and reached to 70% when x = 0.42. Above 0.42, the value of R varied slowly and AI(x)Ga(1-x)N layers almost full relaxed when x = 1 (AIN). In this work the underlying GaN layer was in compressive strain, which resulted in the reduction of lattice misfit between GaN and AlxGa1-xN, and a 570nm AlxGa1-xN layer with the composition of about 0.16 might be grown on GaN coherently from the extrapolation. The different shape of (0004) diffraction peak was discussed to be related to the relaxation. (C) 2004 Elsevier B.V. All rights reserved.
Resumo:
In this paper, we study a problem of geometric inequalities for a Multi-degree of Freedom Neurons. Some new geometric inequalities for a Multi-degree of Freedom Neurons are established. As special cases, some known inequalities are deduced.
Resumo:
In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.
Resumo:
An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.
Resumo:
In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.
Resumo:
This paper discusses the Klein–Gordon–Zakharov system with different-degree nonlinearities in two and three space dimensions. Firstly, we prove the existence of standing wave with ground state by applying an intricate variational argument. Next, by introducing an auxiliary functional and an equivalent minimization problem, we obtain two invariant manifolds under the solution flow generated by the Cauchy problem to the aforementioned Klein–Gordon–Zakharov system. Furthermore, by constructing a type of constrained variational problem, utilizing the above two invariant manifolds as well as applying potential well argument and concavity method, we derive a sharp threshold for global existence and blowup. Then, combining the above results, we obtain two conclusions of how small the initial data are for the solution to exist globally by using dilation transformation. Finally, we prove a modified instability of standing wave to the system under study.