Anticipated Synchronization in a Biologically Plausible Model of Neuronal Motifs

Two identical autonomous dynamical systems coupled in a master-slave configuration can exhibit anticipated synchronization (AS) if the slave also receives a delayed negative self-feedback. Recently, AS was shown to occur in systems of simplified neuron models, requiring the coupling of the neuronal membrane potential with its delayed value. However, this coupling has no obvious biological correlate. Here we propose a canonical neuronal microcircuit with standard chemical synapses, where the delayed inhibition is provided by an interneuron. In this biologically plausible scenario, a smooth transition from delayed synchronization (DS) to AS typically occurs when the inhibitory synaptic conductance is increased. The phenomenon is shown to be robust when model parameters are varied within physiological range. Since the DS-AS transition amounts to an inversion in the timing of the pre- and post-synaptic spikes, our results could have a bearing on spike-timing-dependent-plasticity models

Discrete neural compensator algorithm of dynamic in mobile robots using extended Kalman filter

Este artículo presenta el diseño de un algoritmo basado en redes neuronales en tiempo discreto para su aplicación en robótica móvil. También se muestran las condiciones de estabilidad y una evaluación de los resultados. El robot móvil en el cual se aplicó el algoritmo neural posee 2 controladores en cascada, uno para la cinemática y otro para la dinámica; ambos controladores están basados en la linealización por realimentación. El controlador de la dinámica solo posee la información de la dinámica nominal (parámetros). La red neuronal de compensación se adapta para reducir las perturbaciones ocasionadas por las variaciones en la dinámica y las incertidumbres existentes en el modelo, y esas diferencias en la dinámica entre el modelo nominal y el real son aprendidas por una red neuronal RBF (funciones de base radial) usando el filtro de Kalman extendido para el ajuste de los pesos de salida de las funciones de base radial. El algoritmo de compensación neuronal es eficiente, ya que el costo computacional es menor que el necesario para aprender la totalidad de la dinámica y al mismo tiempo posee la robustez que podría aprender la totalidad de la dinámica en caso de fallo del controlador dinámico. En este trabajo se muestra un análisis de estabilidad del algoritmo neuronal adaptable, y además se comprueba que los errores de control están acotados en función del error de aproximación de la red neuronal RBF. Se muestran resultados de experimentación sobre un robot móvil que prueban la viabilidad práctica y el rendimiento para el control de los mismos.This paper presents the design of an algorithm based on neural networks in discrete time for its application in mobile robots. In addition, the system stability is analyzed and an evaluation of the experimental results is shown. The mobile robot has two controllers, one addressed for the kinematics and the other one designed for the dynamics. Both controllers are based on the feedback linearization. The controller of the dynamics only has information of the nominal dynamics (parameters). The neural algorithm of compensation adapts its behaviour to reduce the perturbations caused by the variations in the dynamics and the model uncertainties. Thus, the differences in the dynamics between the nominal model and the real one are learned by a neural network RBF (radial basis functions) where the output weights are set using the extended Kalman filter. The neural compensation algorithm is efficient, since the consumed processing time is lower than the one required to learning the totality of the dynamics. In addition, the proposed algorithm is robust with respect to failures of the dynamic controller. In this work, a stability analysis of the adaptable neural algorithm is shown and it is demonstrated that the control errors are bounded depending on the error of approximation of the neural network RBF. Finally, the results of experiments performed by using a mobile robot are shown to test the viability in practice and the performance for the control of robots.Peer Reviewe

Superconducting tunable flux qubit with direct readout scheme

We describe a simple and efficient scheme for the readout of a tunable flux qubit, and present preliminary experimental tests for the preparation, manipulation and final readout of the qubit state, performed in incoherent regime at liquid Helium temperature. The tunable flux qubit is realized by a double SQUID with an extra Josephson junction inserted in the large superconducting loop, and the readout is performed by applying a current ramp to the junction and recording the value for which there is a voltage response, depending on the qubit state. This preliminary work indicates the feasibility and efficiency of the scheme.Comment: 10 pages, 5 figure

Electroosmotic Flow and Its Contribution to Iontophoretic Delivery

Iontophoresis is the movement of charged molecules in solution under applied current using pulled multi-barrel glass capillaries drawn to a sharp tip. The technique is generally non-quantitative, and to address this, we have characterized the ejection of charged and neutral species using carbon-fiber electrodes attached to iontophoretic barrels. Our results show that observed ejections are due to the sum of iontophoretic and electroosmotic forces. Using the neutral, electroactive molecule 2-(4-nitrophenoxy) ethanol (NPE), which is only transported by electroosmotic flow (EOF), a positive correlation between the amount ejected and the diameter of each barrel's tip was found. In addition, using various charged and neutral electroactive compounds we found that, when each compound is paired with the EOF marker, the percentage of the ejection due to EOF remains constant. This percentage varies for each pair of compounds, and the differences in mobility are positively correlated to differences in electrophoretic mobility. Overall, the results show that capillary electrophoresis (CE) can be used to predict the percentage of ejection that will be due to EOF. With this information, quantitative iontophoresis is possible for electrochemically inactive drugs by using NPE as a marker for EOF