Stable Irregular Dynamics in Complex Neural Networks

For infinitely large sparse networks of spiking neurons mean field theory shows that a balanced state of highly irregular activity arises under various conditions. Here we analytically investigate the microscopic irregular dynamics in finite networks of arbitrary connectivity, keeping track of all individual spike times. For delayed, purely inhibitory interactions we demonstrate that the irregular dynamics is not chaotic but rather stable and convergent towards periodic orbits. Moreover, every generic periodic orbit of these dynamical systems is stable. These results highlight that chaotic and stable dynamics are equally capable of generating irregular activity.Comment: 10 pages, 2 figure

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

How Chaotic is the Balanced State?

Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under a wide range of conditions. It occurs likewise in sparsely connected random networks that receive excitatory external inputs and recurrent inhibition as well as in networks with mixed recurrent inhibition and excitation. Here we analytically investigate this irregular dynamics in finite networks keeping track of all individual spike times and the identities of individual neurons. For delayed, purely inhibitory interactions we show that the irregular dynamics is not chaotic but stable. Moreover, we demonstrate that after long transients the dynamics converges towards periodic orbits and that every generic periodic orbit of these dynamical systems is stable. We investigate the collective irregular dynamics upon increasing the time scale of synaptic responses and upon iteratively replacing inhibitory by excitatory interactions. Whereas for small and moderate time scales as well as for few excitatory interactions, the dynamics stays stable, there is a smooth transition to chaos if the synaptic response becomes sufficiently slow (even in purely inhibitory networks) or the number of excitatory interactions becomes too large. These results indicate that chaotic and stable dynamics are equally capable of generating the irregular neuronal activity. More generally, chaos apparently is not essential for generating the high irregularity of balanced activity, and we suggest that a mechanism different from chaos and stochasticity significantly contributes to irregular activity in cortical circuits

Streaking temporal double slit interference by an orthogonal two-color laser field

We investigate electron momentum distributions from single ionization of Ar by two orthogonally polarized laser pulses of different color. The two-color scheme is used to experimentally control the interference between electron wave packets released at different times within one laser cycle. This intracycle interference pattern is typically hard to resolve in an experiment. With the two-color control scheme these features become the dominant contribution to the electron momentum distribution. Furthermore the second color can be used for streaking of the otherwise interfering wave packets establishing a which-way marker. Our investigation shows that the visibility of the interference fringes depends on the degree of the which-way information determined by the controllable phase between the two pulses.Comment: submitted to PR

Komponenten für kooperative Intrusion-Detection in dynamischen Koalitionsumgebungen

Koalitionsumgebungen sollen für alle miteinander kooperierenden Mitglieder einen Vorteil bei der Verfolgung eines gemeinsamen Ziels erbringen. Dies gilt für die verschiedensten Anwendungsbereiche, etwa bei kooperierenden Strafverfolgungsbehörden, Wirtschaftsunternehmen oder Streitkräfte. Auch bei der Erkennung von sicherheitsrelevanten Vorgängen in vernetzten Computersystemen erhofft man sich von der Zusammenarbeit eine verbesserte Erkennungsfähigkeit sowie eine schnelle und koordinierte Reaktion auf Einbruchsversuche. Dieser Beitrag stellt verschiedene praxisorientierte Werkzeuge für die koalitionsweite Vernetzung von Ereignismeldungs-produzierenden Sicherheitswerkzeugen vor, die wesentliche Probleme des Anwendungsszenarios lösen helfen: Frühzeitige Anomaliewarnung – ein graphbasierter Anomaliedetektor wird als adaptives Frühwarnmodul für großflächige und koordinierte Angriffe, z.B. Internet-Würmer, eingesetzt. Informationsfilterung – Meldungen werden beim Verlassen der lokalen Domäne entsprechend der domänenspezifischen Richtlinien zur Informationsweitergabe modifiziert (d.h. insbesondere anonymisiert bzw. pseudonymisiert). Datenreduktion – zusätzliche Filter zur Datenreduzierung auf der Basis von vordefinierten Abhängigkeitsregeln steigern die Handhabbarkeit des Datenflusses. Die Funktionsfähigkeit der genannten Komponenten wird derzeit in Form einer prototypischen Implementierung eines Meta-IDS für dynamische Koalitionsumgebungen nachgewiesen

Two-Photon 3D Laser Printing Inside Synthetic Cells

Toward the ambitious goal of manufacturing synthetic cells from the bottom up, various cellular components have already been reconstituted inside lipid vesicles. However, the deterministic positioning of these components inside the compartment has remained elusive. Here, by using two-photon 3D laser printing, 2D and 3D hydrogel architectures are manufactured with high precision and nearly arbitrary shape inside preformed giant unilamellar lipid vesicles (GUVs). The required water-soluble photoresist is brought into the GUVs by diffusion in a single mixing step. Crucially, femtosecond two-photon printing inside the compartment does not destroy the GUVs. Beyond this proof-of-principle demonstration, early functional architectures are realized. In particular, a transmembrane structure acting as a pore is 3D printed, thereby allowing for the transport of biological cargo, including DNA, into the synthetic compartment. These experiments show that two-photon 3D laser microprinting can be an important addition to the existing toolbox of synthetic biology

Study of a homogeneous QSO sample: relations between the QSO and its host galaxy

We analyse a sample of 69 QSOs which have been randomly selected in a complete sample of 104 QSOs (R<18, 0.142 < z < 0.198). 60 have been observed with the NTT/SUSI2 at La Silla, through two filters in the optical band (WB#655 and V#812), and the remaining 9 are taken from archive databases. The filter V#812 contains the redshifted Hbeta and forbidden [OIII] emission lines, while WB#655 covers a spectral region devoid of emission lines, thus measuring the QSO and stellar continua. The contributions of the QSO and the host are separated thanks to the MCS deconvolution algorithm, allowing a morphological classification of the host, and the computation of several parameters such as the host and nucleus absolute V-magnitude, distance between the luminosity center of the host and the QSO, and colour of the host and nucleus. We define a new asymmetry coefficient, independent of any galaxy models and well suited for QSO host studies. The main results from this study are: (i) 25% of the total number of QSO hosts are spirals, 51% are ellipticals and 60% show signs of interaction; (ii) Highly asymmetric systems tend to have a higher gas ionization level (iii) Elliptical hosts contain a substantial amount of ionized gas, and some show off-nuclear activity. These results agree with hierarchical models merger driven evolution.Comment: accepted for publication in MNRAS, 19 pages, 22 figures, 8 table