Timescale-invariant representation of acoustic communication signals by a bursting neuron

Acoustic communication often involves complex sound motifs in which the relative durations of individual elements, but not their absolute durations, convey meaning. Decoding such signals requires an explicit or implicit calculation of the ratios between time intervals. Using grasshopper communication as a model, we demonstrate how this seemingly difficult computation can be solved in real time by a small set of auditory neurons. One of these cells, an ascending interneuron, generates bursts of action potentials in response to the rhythmic syllable-pause structure of grasshopper calls. Our data show that these bursts are preferentially triggered at syllable onset; the number of spikes within the burst is linearly correlated with the duration of the preceding pause. Integrating the number of spikes over a fixed time window therefore leads to a total spike count that reflects the characteristic syllable-to-pause ratio of the species while being invariant to playing back the call faster or slower. Such a timescale-invariant recognition is essential under natural conditions, because grasshoppers do not thermoregulate; the call of a sender sitting in the shade will be slower than that of a grasshopper in the sun. Our results show that timescale-invariant stimulus recognition can be implemented at the single-cell level without directly calculating the ratio between pulse and interpulse durations

Towards Time-triggered Component-based System Models

International audienceIn this paper, we propose a methodology for producing correct-by-construction Time-Triggered (TT) physical model by starting from a high-level model of the application software in Behaviour, Interaction, Priority (BIP). BIP is a component-based framework with formal semantics that rely on multi-party interactions for synchronizing components. Commonly in TT implementations, processes interact with each other through a communication medium. Our methodology transforms, depending on a user-defined task mapping, high-level BIP models where communication between components is strongly synchronized, into TT physical model that integrates a communication medium. Thus, only inter-task communications and components participating in such interactions are concerned by the transformation process. The transformation consists of: (1) breaking atomicity of actions in components by replacing strong synchronizations with asynchronous send/receive interactions, (2) inserting communication media that coordinate execution of inter-task interactions according to a user-defined task mapping, (3) extending the model with an algorithm for handling conflicts between different communication media and (4) instantiating task components and adding local priority rules for handling conflicts between inter-task and intra-task interactions. We also prove the correctness of our transformation, which preserves safety properties. I. INTRODUCTION A Time-Triggered (TT) system initiates all system activities-task activation, message transmission, and message detection-at predetermined points in time. Ideally, in a time-triggered operating system there is only one interrupt signal: the ticks generated by the local periodic clock. These statically defined activation instants enforce regularity and make TT systems more predictable than Event-Triggered (ET) systems. This approach is well-suited for hard real-time systems. In [1] and [2], Kopetz presents an approach for real-time system design based on the TT paradigm which comprises three essential elements: The global notion of time: It must be established by a periodic clock synchronization in order to enable a TT communication and computation, The temporal control structure of each task: In a sequence of computational or communication processes (called tasks), the start of a task is triggered by the progression of the global time, independently from the involved data of the task. The worst-case execution time and thus the worst-case termination instant are also assumed to be known a priori. These statically predefined start and worst-case termination instants, define the temporal control structure of the task

Scheduling in TSN networks using machine learning

The massive adoption of Ethernet technology in multiple sectors, produces the need to provide deterministic solutions to ensure a Quality of Service (QoS) that meets the requirements of time-triggered flows. For this, the Time-Sensitive Networking (TSN) Task Group (TG) of the IEEE 802.1 developed a set of standards that define mechanisms for time-sensitive transmissions of data over Ethernet networks. This project focuses on studying the feasibility of scheduling three classes of time-triggered flows with different time constraints over a simple network topology, which is made from two TSN (Time-Sensitive Networking) nodes connected through a link. Scheduling multiple time-triggered flows is a complex problem because the scheduling, if exists, must meet the time constraints of all these flows. To address this challenge, we explore the potential of using supervised machine learning classification models to accurately predict the feasibility of scheduling a given set of time-triggered flows, meeting their time-constraints, in a Time-Sensitive Network (TSN). Supervised models require a training dataset that contains a data matrix and a class label vector. To obtain the class label vector of each observation, we use an adaptation of the implementation developed in [27] of the Integer Linear Programming (ILP) model introduced in [33]. Two different models are considered: K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM). These algorithms are tested and built from the application of the Leave One Out Cross-Validation (LOOCV) technique with the generated datasets, and the results obtained are compared and discussed. Finally, a hybrid verification strategy is proposed to train and test machine learning models, drastically reducing the resources and computation time originally required to compute the class label of each observation of the dataset

Design and implementation of the land surface model NaturalEnvironment within the generic framework OpenDanubia for integrative, distributed environmental modelling

The project GLOWA-Danube (http://www.glowa-danube.de) aimed at investigating the manifold consequences of Global Change on regional water resources in the Upper Danube Basin. In order to achieve this task, an interdisciplinary, university-based network of experts developed the integrative Decision Support System OpenDanubia (OD). The common base for implementing and coupling the various scientific model components is a generic framework, which provides the coordination of the coupled models that run in parallel exchanging iteratively data via their interfaces. The OD framework takes care of technical aspects, such as ordered data exchange between sub-models, data aggregation, data output, model parallelization and data distribution over the network, which means that model developers do not have to be concerned about complexities evolving from coupling their models. Within this framework the sub-model NaturalEnvironment, representing a land surface model, was developed and implemented. The object-oriented design of this sub-model facilitates a plain, logical representation of the actual physical processes simulated by the sub-model. Physical processes to be modelled are organized in naturally ordered, exchangeable lists that are executed on each spatial computation unit for each modelling time step, depending on their land cover. The type of land cover to be simulated on each freely defined spatial unit is distinguished by one of the three types aquatic, terrestrial and glacier. Additionally, the type terrestrial is influenced by dynamic land use changes which can be triggered e.g. by the socio-economic OD sub-model Farming. This paper presents the basic design of the open source (GPL'ed) OD framework and highlights the implementation of the sub-model NaturalEnvironment within this framework, as well as its interactions with other components included in OD

Logic Circuits Based on Extended Molecular Spider Systems

Spatial locality brings the advantages of computation speed-up and sequence reuse to molecular computing. In particular, molecular walkers that undergo localized re- actions are of interest for implementing logic computations at the nanoscale. We use molecular spider walkers to implement logic circuits. We develop an extended multi- spider model with a dynamic environment wherein signal transmission is triggered via localized reactions, and use this model to implement three basic gates (AND, OR, and NOT) and a cascading mechanism. We develop an algorithm to automatically generate the layout of the circuit. We use a kinetic Monte Carlo algorithm to simulate circuit computations, and we analyze circuit complexity: our design scales linearly with formula size and has a logarithmic time complexity

Efficient reachability analysis of parametric linear hybrid systems with time-triggered transitions

Efficiently handling time-triggered and possibly nondeterministic switches for hybrid systems reachability is a challenging task. In this paper we present an approach based on conservative set-based enclosure of the dynamics that can handle systems with uncertain parameters and inputs, where the uncertainties are bound to given intervals. The method is evaluated on the plant model of an experimental electro-mechanical braking system with periodic controller. In this model, the fast-switching controller dynamics requires simulation time scales of the order of nanoseconds. Accurate set-based computations for relatively large time horizons are known to be expensive. However, by appropriately decoupling the time variable with respect to the spatial variables, and enclosing the uncertain parameters using interval matrix maps acting on zonotopes, we show that the computation time can be lowered to 5000 times faster with respect to previous works. This is a step forward in formal verification of hybrid systems because reduced run-times allow engineers to introduce more expressiveness in their models with a relatively inexpensive computational cost.Comment: Submitte

Hydrothermal Scheduling in the Continuous-Time Framework

Continuous-time optimization models have successfully been used to capture the impact of ramping limitations in power systems. In this paper, the continuous-time framework is adapted to model flexible hydropower resources interacting with slow-ramping thermal generators to minimize the hydrothermal system cost of operation. To accurately represent the non-linear hydropower production function with forbidden production zones, binary variables must be used when linearizing the discharge variables and the continuity constraints on individual hydropower units must be relaxed. To demonstrate the performance of the proposed continuous-time hydrothermal model, a small-scale case study of a hydropower area connected to a thermal area through a controllable high-voltage direct current (HVDC) cable is presented. Results show how the flexibility of the hydropower can reduce the need for ramping by thermal units triggered by intermittent renewable power generation. A reduction of 34% of the structural imbalances in the system is achieved by using the continuous-time model.Comment: Accepted for publication through the Power Systems Computation Conference 202