On the spectra of certain integro-differential-delay problems with applications in neurodynamics

We investigate the spectrum of certain integro-differential-delay equations (IDDEs) which arise naturally within spatially distributed, nonlocal, pattern formation problems. Our approach is based on the reformulation of the relevant dispersion relations with the use of the Lambert function. As a particular application of this approach, we consider the case of the Amari delay neural field equation which describes the local activity of a population of neurons taking into consideration the finite propagation speed of the electric signal. We show that if the kernel appearing in this equation is symmetric around some point a= 0 or consists of a sum of such terms, then the relevant dispersion relation yields spectra with an infinite number of branches, as opposed to finite sets of eigenvalues considered in previous works. Also, in earlier works the focus has been on the most rightward part of the spectrum and the possibility of an instability driven pattern formation. Here, we numerically survey the structure of the entire spectra and argue that a detailed knowledge of this structure is important within neurodynamical applications. Indeed, the Amari IDDE acts as a filter with the ability to recognise and respond whenever it is excited in such a way so as to resonate with one of its rightward modes, thereby amplifying such inputs and dampening others. Finally, we discuss how these results can be generalised to the case of systems of IDDEs

Structure of the WipA protein reveals a novel tyrosine protein phosphatase effector from Legionella pneumophila

Legionnaires' disease is a severe form of pneumonia caused by the bacterium Legionella pneumophila. L. pneumophila pathogenicity relies on secretion of more than 300 effector proteins by a type IVb secretion system. Among these Legionella effectors, WipA has been primarily studied because of its dependence on a chaperone complex, IcmSW, for translocation through the secretion system, but its role in pathogenicity has remained unknown. In this study, we present the crystal structure of a large fragment of WipA, WipA435. Surprisingly, this structure revealed a serine/threonine phosphatase fold that unexpectedly targets tyrosine-phosphorylated peptides. The structure also revealed a sequence insertion that folds into an α-helical hairpin, the tip of which adopts a canonical coiled-coil structure. The purified protein was a dimer whose dimer interface involves interactions between the coiled coil of one WipA molecule and the phosphatase domain of another. Given the ubiquity of protein-protein interaction mediated by interactions between coiled-coils, we hypothesize that WipA can thereby transition from a homodimeric state to a heterodimeric state in which the coiled-coil region of WipA is engaged in a protein-protein interaction with a tyrosine-phosphorylated host target. In conclusion, these findings help advance our understanding of the molecular mechanisms of an effector involved in Legionella virulence and may inform approaches to elucidate the function of other effectors

Chains of rotational tori and filamentary structures close to high multiplicity periodic orbits in a 3D galactic potential

This paper discusses phase space structures encountered in the neighborhood of periodic orbits with high order multiplicity in a 3D autonomous Hamiltonian system with a potential of galactic type. We consider 4D spaces of section and we use the method of color and rotation [Patsis and Zachilas 1994] in order to visualize them. As examples we use the case of two orbits, one 2-periodic and one 7-periodic. We investigate the structure of multiple tori around them in the 4D surface of section and in addition we study the orbital behavior in the neighborhood of the corresponding simple unstable periodic orbits. By considering initially a few consequents in the neighborhood of the orbits in both cases we find a structure in the space of section, which is in direct correspondence with what is observed in a resonance zone of a 2D autonomous Hamiltonian system. However, in our 3D case we have instead of stability islands rotational tori, while the chaotic zone connecting the points of the unstable periodic orbit is replaced by filaments extending in 4D following a smooth color variation. For more intersections, the consequents of the orbit which started in the neighborhood of the unstable periodic orbit, diffuse in phase space and form a cloud that occupies a large volume surrounding the region containing the rotational tori. In this cloud the colors of the points are mixed. The same structures have been observed in the neighborhood of all m-periodic orbits we have examined in the system. This indicates a generic behavior.Comment: 12 pages,22 figures, Accepted for publication in the International Journal of Bifurcation and Chao

A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning

The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human–machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency

New approaches for studying cortical representations

All rights reserved. We review two new approaches for studying cortical representations of sensory stimuli. These exploit optimization algorithms and auto-encoders from machine learning and high resolution electrophysiology data. We show how these approaches can shed new light into the information processing and maintenance taking place in neuronal populations. These approaches allow us to study: 1. Changes in the precision of error representations as a result of neuromodulation. 2. Differences in the cortical connectivity underlying memory representations for different stimuli

Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning.

The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. Our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of "Machine Intelligence", which exposes certain vulnerabilities in its current legal interpretation. Most importantly, it further helps us to explore whether the argument for "artificial personhood" holds any water. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of Human - Machine interaction and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of agency and dynamic tasks carried out by software agents. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Is "artificial personhood" a sufficient legal response to highly sophisticated machine learning techniques employed in decision making that successfully emulate or even enhance human cognitive abilities

Sensory processing and categorization in cortical and deep neural networks

Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results

On conductance-based neural field models

This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamics—based on transmembrane currents—with neural field equations, describing the propagation of spikes over the cortical surface. This model allows for fairly realistic inter-and intra-laminar intrinsic connections that underlie spatiotemporal neuronal dynamics. We focus on the response functions of expected neuronal states (such as depolarization) that generate observed electrophysiological signals (like LFP recordings and EEG). These response functions characterize the model's transfer functions and implicit spectral responses to (uncorrelated) input. Our main finding is that both the evoked responses (impulse response functions) and induced responses (transfer functions) show qualitative differences depending upon whether one uses a neural mass or field model. Furthermore, there are differences between the equivalent convolution and conductance models. Overall, all models reproduce a characteristic increase in frequency, when inhibition was increased by increasing the rate constants of inhibitory populations. However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the opposite effect. These differences suggest that conductance based field models may be important in empirical studies of cortical gain control or pharmacological manipulations

Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1–3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC–FEF–LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load

Rcf2 revealed in cryoEM structures of hypoxic isoforms of mature mitochondrial III-IV supercomplexes

The organisation of the mitochondrial electron transport chain proteins into supercomplexes (SCs) is now undisputed, however their assembly process, or the role of differential expression isoforms, have yet to be determined. In Saccharomyces cerevisiae, cytochrome c oxidase (CIV) forms SCs of varying stoichiometry with cytochrome bc1 (CIII). Recent studies have revealed, in normoxic condition of growth, an interface made exclusively by Cox5A, the only yeast respiratory protein that exists as one of two isoforms depending on oxygen levels. Here, we present the cryo-EM structures of the III2-IV1 and III2-IV2 SCs containing the hypoxic isoform Cox5B solved at 3.4 and 2.8 Å, respectively. We show that the change of isoform doesn’t affect SC formation or activity and that SC stoichiometry is dictated by the level of CIII/CIV biosynthesis. Comparison of the CIV5B and CIV5A-containing SC structures highlighted few differences, mainly found in the region of Cox5. Additional density was revealed in all SCs, independent of CIV isoform, in a pocket formed by Cox1, Cox3, Cox12 and Cox13, away from the CIII-CIV interface. In the CIV5B-containing hypoxic SCs, this could be confidently assigned to the hypoxia-induced gene 1 (Hig1) type 2 protein Rcf2. With conserved residues in mammalian Hig1 proteins and Cox3/Cox12/Cox13 orthologues, we propose that Hig1 type 2 proteins are stoichiometric subunits of CIV, at least when within a III-IV SC