Spin susceptibility in bilayered cuprates: resonant magnetic excitations

We study the momentum and frequency dependence of the dynamical spin susceptibility in the superconducting state of bilayer cuprate superconductors. We show that there exists a resonance mode in the odd as well as the even channel of the spin susceptibility, with the even mode being located at higher energies than the odd mode. We demonstrate that this energy splitting between the two modes arises not only from a difference in the interaction, but also from a difference in the free-fermion susceptibilities of the even and odd channels. Moreover, we show that the even resonance mode disperses downwards at deviations from ${\bf Q}=(\pi,\pi)$. In addition, we demonstrate that there exists a second branch of the even resonance, similar to the recently observed second branch (the $Q^*$-mode) of the odd resonance. Finally, we identify the origin of the qualitatively different doping dependence of the even and odd resonance. Our results suggest further experimental test that may finally resolve the long-standing question regarding the origin of the resonance peak.Comment: 8 pages, 5 figure

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity

Deep temporal models and active inference

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively

Learning Styles and Strategies for Language Use in the Context of Academic Reading Tasks

Although research has indicated that learning styles influence language learning strategy choices, many studies regard the two in isolation from each other. Additionally, most research in these areas is based on large-scale survey instruments that are removed from the context of language learning and use. This study represents an attempt to resolve these issues through two case studies of international students\u27 learning strategy use on tasks in professional graduate programs in the US. Data gathered from interviews, documents, and task logs were analyzed first for strategy use on specific tasks, then for patterns that may indicate consistency according to learning style. The findings indicate that the participants\u27 learning styles provide more predictability in strategy use on particular tasks than other factors such as discipline