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

One month of cocaine abstinence potentiates rapid dopamine signaling in the nucleus accumbens core

Cocaine addiction is a chronic relapsing disorder that is difficult to treat in part because addicts relapse even after extended periods of abstinence. Given the importance of the mesolimbic dopamine (DA) system in drug addiction, we sought to characterize cocaine abstinence induced changes in rapid DA signaling in the nucleus accumbens (NAc). Here, rats were trained to self-administer cocaine for 14 consecutive days, then divided into two groups. Day 1 rats (D1; n = 7) underwent 24 hours of abstinence; Day 30 rats (D30; n = 7) underwent one month of abstinence. After abstinence, all rats underwent a single extinction session. Immediately after, rats were deeply anesthetized and fast scan cyclic voltammetry (FSCV) was used to measure DA release and uptake dynamics in the NAc core before and following a single cocaine injection. We show that one month of cocaine abstinence potentiates the peak concentration of electrically evoked DA in the NAc core following an acute injection of cocaine. This potentiation is not related to alterations in DA uptake parameters, which are unchanged following abstinence, but may reflect alterations in release. These results further support the abundance of literature showing that cocaine abstinence induces neuroplasticity in brain areas implicated in drug reward and relapse. The present findings also demonstrate critical differences between abstinence-induced neuroadaptations in DA signaling and those caused by drug exposure itself