Sneutrino Inflation with Asymmetric Dark Matter

The asymmetric dark matter scenario is known to give an interesting solution for the cosmic coincidence problem between baryon and dark matter densities. In the scenario, the dark matter asymmetry, which is translated to the dark matter density in the present universe, is transferred from the B-L asymmetry generated in the early universe. On the other hand, the generation of the B-L asymmetry is simply assumed, though many mechanisms for the generation are expected to be consistent with the scenario. We show that the generation of the asymmetry in the sneutrino inflation scenario works similarly to the asymmetric dark matter scenario, because the non-renormalizable operator which translates the B-L asymmetry to the dark matter asymmetry is naturally obtained by integrating right-handed neutrinos out. As a result, important issues concerning cosmology (inflation, the mass density of dark matter, and the baryon asymmetry of the universe) as well as neutrino masses and mixing have a unified origin, namely, the right-handed neutrinos.Comment: 11 pages, 4 figures; v2: reference added, Fig. 3 changed and explanation added; v3: version accepted for publication in PR

A Computational Predictor of Human Episodic Memory Based on a Theta Phase Precession Network

In the rodent hippocampus, a phase precession phenomena of place cell firing with the local field potential (LFP) theta is called “theta phase precession” and is considered to contribute to memory formation with spike time dependent plasticity (STDP). On the other hand, in the primate hippocampus, the existence of theta phase precession is unclear. Our computational studies have demonstrated that theta phase precession dynamics could contribute to primate–hippocampal dependent memory formation, such as object–place association memory. In this paper, we evaluate human theta phase precession by using a theory–experiment combined analysis. Human memory recall of object–place associations was analyzed by an individual hippocampal network simulated by theta phase precession dynamics of human eye movement and EEG data during memory encoding. It was found that the computational recall of the resultant network is significantly correlated with human memory recall performance, while other computational predictors without theta phase precession are not significantly correlated with subsequent memory recall. Moreover the correlation is larger than the correlation between human recall and traditional experimental predictors. These results indicate that theta phase precession dynamics are necessary for the better prediction of human recall performance with eye movement and EEG data. In this analysis, theta phase precession dynamics appear useful for the extraction of memory-dependent components from the spatio–temporal pattern of eye movement and EEG data as an associative network. Theta phase precession may be a common neural dynamic between rodents and humans for the formation of environmental memories

Research on the making of Japanese language class to build <Authentic Learning>: "Zhuangzi" as a teaching material

This paper summarizes the results of research conducted to clarify the inner facts and methods of creating a Japanese language class that builds "authentic learning". In this research, first of all, we focused on the use of "language perspectives and ideas", which are considered to be unique to Japanese language studies, and searched for the specifics of them using "Zhuangzi" as a teaching material. By focusing on words that are different from previous learning ("人皆" and "此獨") , learning for modern social and cultural problems and individual problems that live in society and groups, it became clear that people can deepen their thoughts

Quo Vadis, Artificial Intelligence?

Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles