Passive interferometric symmetries of multimode Gaussian pure states

As large-scale multimode Gaussian states begin to become accessible in the laboratory, their representation and analysis become a useful topic of research in their own right. The graphical calculus for Gaussian pure states provides powerful tools for their representation, while this work presents a useful tool for their analysis: passive interferometric (i.e., number-conserving) symmetries. Here we show that these symmetries of multimode Gaussian states simplify calculations in measurement-based quantum computing and provide constructive tools for engineering large-scale harmonic systems with specific physical properties, and we provide a general mathematical framework for deriving them. Such symmetries are generated by linear combinations of operators expressed in the Schwinger representation of U(2), called nullifiers because the Gaussian state in question is a zero eigenstate of them. This general framework is shown to have applications in the noise analysis of continuous-various cluster states and is expected to have additional applications in future work with large-scale multimode Gaussian states.Comment: v3: shorter, included additional applications, 11 pages, 7 figures. v2: minor content revisions, additional figures and explanation, 23 pages, 18 figures. v1: 22 pages, 16 figure

Neural field theory of adaptive effects on auditory evoked responses and mismatch negativity in multifrequency stimulus sequences

Physiologically based neural field theory (NFT) of the corticothalamic system, including adaptation, is used to calculate the responses evoked by trains of auditory stimuli that differ in frequency. In oddball paradigms, fully distinguishable frequencies lead to different standard (common stimulus) and deviant (rare stimulus) responses; the signal obtained by subtracting the standard response from the deviant is termed the mismatch negativity (MMN). In this analysis, deviant responses are found to correspond to unadapted cortex, whereas the part of auditory cortex that processes the standard stimuli adapts over several stimulus presentations until the final standard response form is achieved. No higher-order memory processes are invoked. In multifrequency experiments, the deviant response approaches the standard one as the deviant frequency approaches that of the standard and analytic criteria for this effect to be obtained. It is shown that these criteria can also be used to understand adaptation in random tone sequences. A method of probing MMNs and adaptation in random tone sequences is suggested to makes more use of such data

Neural field modeling and analysis of large-scale brain dynamics

The human brain exhibits complex spatiotemporal dynamics at multiple scales. On length scales of a few tenths of a millimeter to the entire brain, neural field theory (NFT) is a well-established physiologically based model of brain activity that has reproduced many key-features of large-scale brain activity such as those measured with electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Within this framework, brain activity can be decomposed using modal analysis which uses the natural modes of oscillations, eigenmodes, of neural activity on the cortical surface as the fundamental building blocks for all activity. This thesis investigates the spatiotemporal dynamics of such eigenmodes and how they can be used to predict and analyze brain activity. The spatiotemporal properties of the nine lowest-order eigenmodes are derived and their relationship to cortical geometry is explored. It is shown that eigenmode beating gives rise to complex wave dynamics (including standing, traveling, and rotating waves) which have been observed experimentally for decades. NFT is also used to explain and analyze experimental observations of large-scale brain dynamics from two distinct areas of neuroscience. Firstly, the phenomenon of perceptual echo, whereby random input stimuli at one location are correlated with EEG responses at other locations, is predicted and analysed. Secondly, inverse modeling of EEG data over the sleep-wake cycle is performed on patients with mild cognitive impairment and Alzheimer's disease in order to infer abnormal underlying physiology in these populations. Abnormalities in corticocortical, corticothalamic, and intrathalamic networks are analyzed with reference to known sleep impairments in these clinical populations and hypothesized mechanisms of sleep disruption. The potential of certain model parameters to serve as biomarkers of disease progression is discussed

D3.4 Final Report: Regularly monitor country-specific progress in enabling new DARIAH membership

This report provides information about activities and progress towards establishing DARIAH membership in six countries: the Czech Republic, Finland, Israel, Spain, Switzerland, and the UK, which took place between July and December 2019. Previous activities were described in detail in the D3.2 - Regularly Monitor Country-Specific Progress in Enabling New DARIAH Membership. During the project lifetime, the Czech Republic joined DARIAH ERIC; in other countries, collaboration with DARIAH has been greatly strengthened and significant progress regarding DARIAH membership has been achieved. The report also outlines the next steps in the accession processes, building on the results of the DESIR project