Sodium and Calcium Currents in Dispersed Mammalian Septal Neurons

Voltage-gated Na+ and Ca2+ conductances of freshly dissociated septal neurons were studied in the whole-cell configuration of the patch-clamp technique. All cells exhibited a large Na+ current with characteristic fast activation and inactivation time courses. Half-time to peak current at -20 mV was 0.44 +/- 0.18 ms and maximal activation of Na+ conductance occurred at 0 mV or more positive membrane potentials. The average value was 91 +/- 32 nS (approximately 11 mS cm-2). At all membrane voltages inactivation was well fitted by a single exponential that had a time constant of 0.44 +/- 0.09 ms at 0 mV. Recovery from inactivation was complete in approximately 900 ms at -80 mV but in only 50 ms at -120 mV. The decay of Na+ tail currents had a single time constant that at -80 mV was faster than 100 microseconds. Depolarization of septal neurons also elicited a Ca2+ current that peaked in approximately 6-8 ms. Maximal peak Ca2+ current was obtained at 20 mV, and with 10 mM external Ca2+ the amplitude was 0.35 +/- 0.22 nA. During a maintained depolarization this current partially inactivated in the course of 200-300 ms. The Ca2+ current was due to the activity of two types of conductances with different deactivation kinetics. At -80 mV the closing time constants of slow (SD) and fast (FD) deactivating channels were, respectively, 1.99 +/- 0.2 and 0.11 +/- 0.03 ms (25 degrees C). The two kinds of channels also differed in their activation voltage, inactivation time course, slope of the conductance-voltage curve, and resistance to intracellular dialysis. The proportion of SD and FD channels varied from cell to cell, which may explain the differential electrophysiological responses of intracellularly recorded septal neurons

Integración y fusión multisensorial en robots móviles autónomos

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Arquitectura de Computadores y Automática, leída el 05-03-1999Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEpu

Thyrotropin-releasing-hormone (TRH) and its physiological metabolite TRH-OH inhibit Na+ channel activity in mammalian septal neurons

The interaction of thyrotropin-releasing hormone (TRH) and its physiological metabolite TRH-OH with Na+ channels was studied in enzymatically dissociated guinea pig septal neurons by using the whole-cell variant of the patch-clamp technique. In about 60% of the cells tested, the neuropeptides at concentrations between 0.01 and 2.5 ,uM produced a dose-dependent reversible attenuation of Na+ currents. With 2 ,uM TRH-OH, peak Na+ current amplitude was reduced by 20-50% (27 ± 8%, mean ± SD; n = 16), whereas at the same concentration TRH was approximately half as effective as TRH-OH. In the presence of the tripeptides, the voltage-dependent parameters of the Na+ current were unaltered. TRH-induced reduction of Na+ current amplitude was transient and recovered almost completely during maintained exposure to the peptides. In addition, the response to either TRH-OH or TRH decreased with repeated treatment. Our results demonstrate that neuronal Na+ channels can be modulated by naturally occurring neuropeptides

ART-GCS: an adaptive real-time multi-agent ground control station

Ground Control Stations (GCS) are essential tools to monitor and command real-world complex missions involving Unmanned Vehicles (UVs). As the number and types of UVs in the mission grows, implementing a robust and adaptable GCS, capable of simplifying and reducing operator' interactions and mental workloads, becomes an engineering challenge. To address it, this paper presents a new Adaptive-Real-Time (ART)-GCS that 1) allows to monitor and control a runtime changing number of heterogeneous UVs, 2) adapt its GUI to the mission requirements and operators workload to minimize their fatigue and stress, and 3) provide support to experiments with actual and simulated UVs. To show its benefits in real-world missions, this paper presents a field experiment where, for safety reasons, a simulated unmanned aerial vehicle has to find an oil-spill that must be enclosed by a containment boom dragged by two real unmanned surface vehicles

A Small Domain in the N Terminus of the Regulatory a-Subunit Kv2.3 Modulates Kv2.1 Potassium Channel Gating

Recent work has demonstrated the existence of regulatory K1 channel a-subunits that are electrically silent but capable of forming heterotetramers with other pore-forming subunits to modify their function. We have investigated the molecular determinant of the modulatory effects of Kv2.3, a silent K1 channel a-subunit specific of brain. This subunit induces on Kv2.1 channels a marked deceleration of activation, inactivation, and closing kinetics. We constructed chimeras of the Kv2.1 and Kv2.3 proteins and analyzed the K1 currents resulting from the coexpression of the chimeras with Kv2.1. The data indicate that a region of 59 amino acids in the N terminus, adjacent to the first transmembrane segment, is the major structural element responsible for the regulatory function of Kv2.3. The sequence of this domain of Kv2.3 is highly divergent compared with the same region in the other channels of the Kv2 family. Replacement of the regulatory fragment of Kv2.3 by the equivalent of Kv2.1 leads to loss of modulatory function, whereas gain of modulatory function is observed when the Kv2.3 fragment is transferred to Kv2.1. Thus, this study identifies a N-terminus domain involved in Kv2.1 channel gating and in the modulation of this channel by a regulatory a-subunit

Minimum time search in real-world scenarios using multiple UAVs with onboard orientable cameras

This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.This work was supported by Airbus under SAVIER AER30459 projec

Unified fusion system based on bayesian networks for autonomous mobile robots

A multisensor fusion system that is usedfor estimating the location of a robot and the state of the objects around is presented. The whole fusion system has been implemented as a Dynamic Bayesian Networks (DBN) with the purpose of having a homogenous and formalized way of capturing the dependencies that exist between the robot location, the state of the environment, and all the sensorial data. At this stage of the research it consists of two independent DBNs, one for estimating the robot location and another for building an occupancy probabilistic map of the environment, which are the basis of a unified fusion system. The dependencies of the variables and information in the two DBN will be captured by a unique DBN constructed by adding arcs (and nodes if necessary) between the two DBN. The DBN implemented so far can be used in robots with different sets of sensors

Identification and Functional Characterization of a K1 Channel a- Subunit with Regulatory Properties Specific to Brain

The physiological diversity of K1 channels mainly depends on the expression of several genes encoding different a-subunits. We have cloned a new K1 channel a-subunit (Kv2.3r) that is unable to form functional channels on its own but that has a major regulatory function. Kv2.3r can coassemble selectively with other a-subunits to form functional heteromultimeric K1 channels with kinetic properties that differ from those of the parent channels. Kv2.3r is expressed exclusively in the brain, being concentrated particularly in neocortical neurons. The functional expression of this regulatory a-subunit represents a novel mechanism without precedents in voltage-gated channels, which might contribute to further increase the functional diversity of K1 channels necessary to specify the intrinsic electrical properties of individual neurons

Multisensor fusion of environment measures using Bayesian Networks

Autonomous mobile robots usually require a large number of sensor types and sensing modules. There are different sensors, some complementary and some redundant. Integrating the sensor measures implies several multisensor fusion techniques. These techniques can be classified in two groups: low level fusion, used for direct integration of sensory data; and high level fusion, which is used for indirect integration of sensory data. We have developed a system to integrate indirect measures of different sensors. This system allows us to use any type of sensor which provides measures of the robot's environment It Is designed as a Belief Bayesian Network. The method needs that the user creates a low level fusion module and an interface between that module and our fusion system

Minimum time search in uncertain dynamic domains with complex sensorial platforms

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models