Location and function of the slow afterhyperpolarization channels in the basolateral amygdala

The basolateral amygdala (BLA) assigns emotional significance to sensory stimuli. This association results in a change in the output (action potentials) of BLA projection neurons in response to the stimulus. Neuronal output is controlled by the intrinsic excitability of the neuron. A major determinant of intrinsic excitability in these neurons is the slow after hyperpolarization (sAHP) that follows action potential (AP) trains and produces spike-frequency adaptation. The sAHP is mediated by a slow calcium-activated potassium current (sI(AHP)), but little is known about the channels that underlie this current. Here, using whole-cell patch-clamp recordings and high-speed calcium imaging from rat BLA projection neurons, we examined the location and function of these channels. We determined the location of the sI(AHP) by applying a hyperpolarizing voltage step during the sI(AHP) and measuring the time needed for the current to adapt to the new command potential, a function of its electrotonic distance from the somatic recording electrode. Channel location was also probed by focally uncaging calcium using a UV laser. Both methodologies indicated that, in BLA neurons, the sI(AHP) is primarily located in the dendritic tree. EPSPs recorded at the soma were smaller, decayed faster, and showed less summation during the sAHP. Adrenergic stimulation and buffering calcium reduced the sAHP and the attenuation of the EPSP during the sAHP. The sAHP also modulated the AP in the dendrite, reducing the calcium response evoked by a single AP. Thus, in addition to mediating spike-frequency adaptation, the sI(AHP) modulates communication between the soma and the dendrite

Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search

In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to move away from the "ten blue links'' metaphor, as mobile users are less likely to click on them, expecting to get the answer directly from the snippets. In turn, this has revived the interest in Question Answering. Then, along came chatbots, conversational systems, and messaging platforms, where the user needs could be better served with the system asking follow-up questions in order to better understand the user's intent. While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent. However, this possibility should not be overused, as this practice could confuse and/or annoy the user. How to produce good variable-length lists, given the conflicting objectives of staying short while maximizing the likelihood of having a correct answer included in the list, is an underexplored problem. It is also unclear how to evaluate a system that tries to do that. Here we aim to bridge this gap. In particular, we define some necessary and some optional properties that an evaluation measure fit for this purpose should have. We further show that existing evaluation measures from the IR tradition are not entirely suitable for this setup, and we propose novel evaluation measures that address it satisfactorily.Comment: 4 pages, in Proceeding of SIGIR 201

Quality Determination of Hydraulic Pumps with Adaptive Fuzzy Pattern Classifiers to Reduce the Risk for Quality Management

Automated production of complex assemblies such as hydraulic pumps also requires reliable detection of defects utilizing functional tests. In principle, this is a classification task in good/bad, which, however, is often not to be made sharply but should provide gradations for detailed error analysis. From this, conclusions can be drawn, for example, about the type or location of the defects, wear, or aging of components in the production chain. A high-dimensional vector of data from static or dynamic measurements including is generally available as the basis for the fault detection model. Modeling such complex nonlinear systems under various load conditions with dynamic test procedures leads to uncertainties that should also be reflected in the diagnostic model. For this, the design of the classification model (the classifier) should be largely automatic during the training phase for time and cost reasons. In addition, online updating under actual operating conditions is also often desired. These challenging goals can be met through the artificial intelligence (AI) methodology of fuzzy pattern classification. This chapter deals with the development of a fuzzy classifier for the application case of the final inspection of hydraulic axial piston pumps. The focus is on the automatic training of the classifier employing a new adaptation procedure and permanently (until termination) evaluates the resp. current classifier using performance measures. Using real experimental data, the procedure and the step-by-step adaptation results for different links between the current classification model and the new data are presented and compared

Drug-Driven AMPA Receptor Redistribution Mimicked by Selective Dopamine Neuron Stimulation

Addictive drugs have in common that they cause surges in dopamine (DA) concentration in the mesolimbic reward system and elicit synaptic plasticity in DA neurons of the ventral tegmental area (VTA). Cocaine for example drives insertion of GluA2-lacking AMPA receptors (AMPARs) at glutamatergic synapes in DA neurons. However it remains elusive which molecular target of cocaine drives such AMPAR redistribution and whether other addictive drugs (morphine and nicotine) cause similar changes through their effects on the mesolimbic DA system

Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior

Midbrain dopamine neurons are crucial for many behavioral and cognitive functions. As the major excitatory input, glutamatergic afferents are important for control of the activity and plasticity of dopamine neurons. However, the role of glutamatergic input as a whole onto dopamine neurons remains unclear. Here we developed a mouse line in which glutamatergic inputs onto dopamine neurons are specifically impaired, and utilized this genetic model to directly test the role of glutamatergic inputs in dopamine-related functions. We found that while motor coordination and reward learning were largely unchanged, these animals showed prominent deficits in effort-related behavioral tasks. These results provide genetic evidence that glutamatergic transmission onto dopaminergic neurons underlies incentive motivation, a willingness to exert high levels of effort to obtain reinforcers, and have important implications for understanding the normal function of the midbrain dopamine system.Fil: Hutchison, M. A.. National Institutes of Health; Estados UnidosFil: Gu, X.. National Institutes of Health; Estados UnidosFil: Adrover, Martín Federico. National Institutes of Health; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Lee, M. R.. National Institutes of Health; Estados UnidosFil: Hnasko, T. S.. University of California at San Diego; Estados UnidosFil: Alvarez, V. A.. National Institutes of Health; Estados UnidosFil: Lu, W.. National Institutes of Health; Estados Unido

Profillinie 4: Kundenorientierte Gestaltung von vernetzten Wertschöpfungsketten:

Wertschöpfungsstrukturen in der Gesellschaft passen sich vor dem Hintergrund globaler Herausforderungen sehr flexibel und dynamisch an ständig neue Anforderungen an. Innovative Wertschöpfung kann dabei immer weniger von einzelnen Akteuren in Wissenschaft, Technik, Wirtschaft und Gesellschaft geleistet werden, sondern verschiedene interdisziplinäre Kompetenzen müssen gebündelt werden, die zielorientierte Vernetzung von Wissen und Ressourcen muss gestaltet werden. Unbestechlicher Maßstab für den Erfolg jeglicher wirtschaftlicher Unternehmung und damit auch von vernetzten Wertschöpfungsketten ist der Kunde mit seinen individuellen Bedürfnissen, das heißt letztlich die Akzeptanz und Absatzchancen von Produkten und Dienstleistungen am Markt