An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Psychedelics disrupt the functional hierarchy of the human brain

Psychedelics, such as lysergic acid diethylamide (LSD), N,N-dimethyltryptamine (DMT), and psilocybin, are hallucinogenic drugs that act on the 5-HT2A receptor. Their effects can be analysed on three different levels: (1) phenomenology, or subjective experience, as psychedelics strongly alter a person’s state of consciousness; (2) neuroimaging, using tools such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG); and (3) pharmacology, or their biochemical interactions with various receptors in the brain. In the last ten years, there has been a considerable rise in psychedelic research due to emerging evidence that they can be effective for treating a wide range of conditions, including depression, substance use disorder, end-of-life anxiety, and post-traumatic stress disorder (PTSD). A popular theory known as RElaxed Beliefs Under pSychedelics (REBUS) posits that psychedelics “flatten” the functional hierarchy of the brain; that is, they diminish the inhibitory effect of higher-order, associative cortical networks on lower-order, sensory networks. However, it is challenging to test REBUS because it proposes that psychedelics modify the weights of the prior beliefs, or “priors,” encoded in higher-order networks, yet there are no robust models that systematically link priors to the activity of these networks, as recorded by MEG or fMRI. Instead, it is more straightforward to measure hierarchy directly on the connectivity of these networks, without assuming that the connectivity instantiates priors. My aims in this thesis are twofold. The first is to provide a comprehensive overview of the current landscape of scientific literature about the effects of psychedelics on healthy humans. In particular, I conduct systematic reviews and meta-analyses of the three levels of analysis mentioned earlier – phenomenology, neuroimaging, and pharmacology – across three different psychedelics (DMT, LSD, and psilocybin). I discover that there are highly nonlinear relationships both between drugs and also between levels of analysis. The second aim is to measure brain hierarchy under psychedelics with simple, empirically tractable methods. In particular, I discuss two different measures: irreversibility, or the asymmetry between sending and receiving neural signals; and trophic coherence, which decomposes networks into hierarchical levels. I apply these two measures to an existing MEG dataset of healthy human participants who were administered LSD. My analyses reveal that LSD decreases both irreversibility and trophic coherence, suggesting that LSD does indeed flatten the functional hierarchy of the brain. Furthermore, the spatial distribution of changes in irreversibility and hierarchical levels on LSD is consistent with the findings of the meta-analysis. In particular, the meta-analysis demonstrates that LSD, more than other psychedelics, alters the quality and intensity of visual hallucinations, while also elevating connectivity between the visual network and other brain networks. LSD reduces irreversibility and top-down hierarchical levels most in the regions of the brain that are involved in visual processing. Taken together, these results indicate that LSD diminishes the influence of visual network activity on hierarchically directed connectivity in the brain, which may explain the profound effects of LSD-induced visual hallucinations on higher-order cognitive processes such as a person’s sense of self. Furthermore, this thesis constitutes the first attempt to measure the hierarchy of directed information flow in the brain under psychedelics. Future work could deploy the same methods to determine whether the flattening of hierarchy predicts the therapeutic response to psychedelics

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described