Refractoriness and neural precision

The response of a spiking neuron to a stimulus is often characterized by its time-varying firing rate, estimated from a histogram of spike times. If the cell's firing probability in each small time interval depends only on this firing rate, one predicts a highly variable response to repeated trials, whereas many neurons show much greater fidelity. Furthermore, the neuronal membrane is refractory immediately after a spike, so that the firing probability depends not only on the stimulus but also on the preceding spike train. To connect these observations, we investigated the relationship between the refractory period of a neuron and its firing precision. The light response of retinal ganglion cells was modeled as probabilistic firing combined with a refractory period: the instantaneous firing rate is the product of a "free firing rate, " which depends only on the stimulus, and a "recovery function," which depends only on the time since the last spike. This recovery function vanishes for an absolute refractory period and then gradually increases to unity. In simulations, longer refractory periods were found to make the response more reproducible, eventually matching the precision of measured spike trains. Refractoriness, although often thought to limit the performance of neurons, may in fact benefit neuronal reliability. The underlying free firing rate derived by allowing for the refractory period often exceeded the observed firing rate by an order of magnitude and was found to convey information about the stimulus over a much wider dynamic range. Thus, the free firing rate may be the preferred variable for describing the response of a spiking neuron.     

Optimal-tuning PID control of adaptive materials for structural efficiency

The objective of this paper was solving the optimization problem of lightweight stiffened structures modelled as a two-dimensional domain in an efficient computational way. The underlying premise was that mass should be distributed in an efficient way, so as to use a minimum amount of material to accomplish the mechanical function. This premise was expressed as a global, multi-objective optimization problem in which stiffness and mass were conflicting objectives. Alternative local evolution rules were implemented to update mass density or Young’s modulus at each step of the iterative procedure. The solution of the structural optimization problem was accomplished by a novel automatic procedure consisting of two consecutive stages of control and optimization. In the first stage of Proportional Integral Derivative (PID) control gains were manually selected whereas in the second stage the finding of optimal values of control gains, target, and cost indices was allowed. In this study a bone-like material was adopted and a thin slab was analysed as a sample problem.     

Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high‐value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long‐term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions. © 2011 Wiley Periodicals, Inc.     

Selective voltammetric determination of electroactive neuromodulating species in biological samples using iron(II) phthalocyanine modified multi-wall carbon nanotubes paste electrode

Iron(II) phthalocyanine (FePc) modified multi-wall carbon nanotubes paste electrodes (MWCNTPEs) were used as voltammetric sensors to selectively detect dopamine (DA) in the presence of serotonin (5-HT). The electrochemical behavior of DA at the new modified electrode was investigated using CV. The enhanced current response of DA indicates that FePc modification of the MWCNTPE surface results in a high catalytic activity for the redox reaction of DA. Differential pulse voltammetry was applied in detection of DA and 5-HT at FePc-MWCNTPE. The method parameters were optimized. Detection limit of 2.05 × 10⁻⁷ M was obtained for DA by using the electrocatalytic oxidation signal corresponding to the Fe^II/Fe^III redox process. The separation between the peak potentials of DA and 5-HT is 170 mV which is large enough for the simultaneously, selective determination of the two chemical species in their mixtures. There was no electrochemical response for ascorbic acid (AA) added in the sample. The monoamine neurotransmitter measuring method has been tested in analyzing deproteinized serum samples.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Rule-based tools for the configuration of ambient intelligence systems: a comparative user study

Multimedia Tools and Applications - This paper describes a 63-participant user study that compares two widely known systems supporting end users in creating trigger-action rules for the Internet of...     

Managing the requirements flow from strategy to release in large-scale agile development: a case study at Ericsson

In a large organization, informal communication and simple backlogs are not sufficient for the management of requirements and development work. Many large organizations are struggling to successfully adopt agile methods, but there is still little scientific knowledge on requirements management in large-scale agile development organizations. We present an in-depth study of an Ericsson telecommunications node development organization which employs a large scale agile method to develop telecommunications system software. We describe how the requirements flow from strategy to release, and related benefits and problems. Data was collected by 43 interviews, which were analyzed qualitatively. The requirements management was done in three different processes, each of which had a different process model, purpose and planning horizon. The release project management process was plan-driven, feature development process was continuous and implementation management process was agile. The perceived benefits included reduced development lead time, increased flexibility, increased planning efficiency, increased developer motivation and improved communication effectiveness. The recognized problems included difficulties in balancing planning effort, overcommitment, insufficient understanding of the development team autonomy, defining the product owner role, balancing team specialization, organizing system-level work and growing technical debt. The study indicates that agile development methods can be successfully employed in organizations where the higher level planning processes are not agile. Combining agile methods with a flexible feature development process can bring many benefits, but large-scale software development seems to require specialist roles and significant coordination effort.     

Graphs of linear clique-width at most 3

A graph has linear clique-width at most k if it has a clique-width expression using at most k labels such that every disjoint union operation has an operand which is a single vertex graph. We give the first characterisation of graphs of linear clique-width at most 3, and we give the first polynomial-time recognition algorithm for graphs of linear clique-width at most 3. In addition, we present new characterisations of graphs of linear clique-width at most 2. We also give a layout characterisation of graphs of bounded linear clique-width; a similar characterisation was independently shown by Gurski and by Lozin and Rautenbach.     

An efficient deconvolution algorithm for estimating oxygen consumption during muscle activities

The reconstruction of an unknown input function from noisy measurements in a biological system is an ill-posed inverse problem. Any computational algorithm for its solution must use some kind of regularization technique to neutralize the disastrous effects of amplified noise components on the computed solution. In this paper, following a hierarchical Bayesian statistical inversion approach, we seek estimates for the input function and regularization parameter (hyperparameter) that maximize the posterior probability density function. We solve the maximization problem simultaneously for all unknowns, hyperparameter included, by a suitably chosen quasi-Newton method. The optimization approach is compared to the sampling-based Bayesian approach. We demonstrate the efficiency and robustness of the deconvolution algorithm by applying it to reconstructing the time courses of mitochondrial oxygen consumption during muscle state transitions (e.g., from resting state to contraction and recovery), from the simulated noisy output of oxygen concentration dynamics on the muscle surface. The model of oxygen transport and metabolism in skeletal muscle assumes an in vitro cylindrical structure of the muscle in which the oxygen from the surrounding oxygenated solution diffuses into the muscle and is then consumed by the muscle mitochondria. The algorithm can be applied to other deconvolution problems by suitably replacing the forward model of the system.