Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.Comment: This paper is accepted the 24th International Conference On Neural Information Processing (ICONIP 2017). The previous submission to arXiv is replaced by this version because there was an error in Equation

Giant Rashba splitting of quasi-1D surface states on Bi/InAs(110)-(2×\times1)

Electronic states on the Bi/InAs(110)-(2$\times$1) surface and its spin-polarized structure are revealed by angle-resolved photoelectron spectroscopy (ARPES), spin-resolved ARPES, and density-functional-theory calculation. The surface state showed quasi-one-dimensional (Q1D) dispersion and a nearly metallic character; the top of the hole-like surface band is just below the Fermi level. The size of the Rashba parameter ($\alpha_{\rm R}$) reached quite a large value ($\sim$5.5 eV\AA). The present result would provide a fertile playground for further studies of the exotic electronic phenomena in 1D or Q1D systems with the spin-split electronic states as well as for advanced spintronic devices.Comment: 8 pages (double column), 7 figures and 1 tabl

Glycosylation pattern of brush border-associated glycoproteins in enterocyte-like cells: involvement of complex-type N-glycans in apical trafficking

We have previously reported that galectin-4, a tandem repeat-type galectin, regulates the raft-dependent delivery of glycoproteins to the apical brush border membrane of enterocyte-like HT-29 cells. N-Acetyllactosamine-containing glycans, known as galectin ligands, were found enriched in detergent-resistant membranes. Here, we analyzed the potential contribution of N-and/ or O-glycans in this mechanism. Structural studies were carried out on the brush border membrane-enriched fraction using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and nano-ESI-QTOF-MS/MS. The pattern of N-glycans was very heterogeneous, with the presence of high mannose- and hybrid-type glycans as well as a multitude of complex-type glycans. In contrast, the pattern of O-glycans was very simple with the presence of two major core type 1 O-glycans, sialylated and bisialylated T-antigen structures {[}Neu5Ac alpha 2-3Gal beta 1-3GalNAc-ol and Neu5Ac alpha 2-3Gal beta 1 -3(Neu5Ac alpha 2-6)GalNAc-ol]. Thus, N-glycans rather than O-glycans contain the N-acetyllactosamine recognition signals for the lipid raft-based galectin-4-dependent apical delivery. In the presence of 1-deoxymannojirimycin, a drug which inhibits the generation of hybrid-type or complex type N-glycans, the extensively O-glycosylated mucin-like MUC1 glycoprotein was not delivered to the apical brush border but accumulated inside the cells. Altogether, our data demonstrate the crucial role of complex N-glycans in the galectin-4-dependent delivery of glycoproteins to the apical brush border membrane of enterocytic HT-29 cells