Sweet taste and nutrient value subdivide rewarding dopaminergic neurons in Drosophila

Dopaminergic neurons provide reward learning signals in mammals and insects [1-4]. Recent work in Drosophila has demonstrated that water-reinforcing dopaminergic neurons are different to those for nutritious sugars [5]. Here, we tested whether the sweet taste and nutrient properties of sugar reinforcement further subdivide the fly reward system. We found that dopaminergic neurons expressing the OAMB octopamine receptor [6] specifically convey the short-term reinforcing effects of sweet taste [4]. These dopaminergic neurons project to the beta\u272 and gamma4 regions of the mushroom body lobes. In contrast, nutrient-dependent long-term memory requires different dopaminergic neurons that project to the gamma5b regions, and it can be artificially reinforced by those projecting to the beta lobe and adjacent alpha1 region. Surprisingly, whereas artificial implantation and expression of short-term memory occur in satiated flies, formation and expression of artificial long-term memory require flies to be hungry. These studies suggest that short-term and long-term sugar memories have different physiological constraints. They also demonstrate further functional heterogeneity within the rewarding dopaminergic neuron population

Early calcium increase triggers the formation of olfactory long-term memory in honeybees

<p>Abstract</p> <p>Background</p> <p>Synaptic plasticity associated with an important wave of gene transcription and protein synthesis underlies long-term memory processes. Calcium (Ca²⁺) plays an important role in a variety of neuronal functions and indirect evidence suggests that it may be involved in synaptic plasticity and in the regulation of gene expression correlated to long-term memory formation. The aim of this study was to determine whether Ca²⁺is necessary and sufficient for inducing long-term memory formation. A suitable model to address this question is the Pavlovian appetitive conditioning of the proboscis extension reflex in the honeybee <it>Apis mellifera, </it>in which animals learn to associate an odor with a sucrose reward.</p> <p>Results</p> <p>By modulating the intracellular Ca²⁺concentration ([Ca²⁺]i) in the brain, we show that: (i) blocking [Ca²⁺]i increase during multiple-trial conditioning selectively impairs long-term memory performance; (ii) conversely, increasing [Ca²⁺]i during single-trial conditioning triggers long-term memory formation; and finally, (iii) as was the case for long-term memory produced by multiple-trial conditioning, enhancement of long-term memory performance induced by a [Ca²⁺]i increase depends on <it>de novo </it>protein synthesis.</p> <p>Conclusion</p> <p>Altogether our data suggest that during olfactory conditioning Ca²⁺is both a necessary and a sufficient signal for the formation of protein-dependent long-term memory. Ca²⁺therefore appears to act as a switch between short- and long-term storage of learned information.</p

Integration of Parallel Opposing Memories Underlies Memory Extinction.

Accurately predicting an outcome requires that animals learn supporting and conflicting evidence from sequential experience. In mammals and invertebrates, learned fear responses can be suppressed by experiencing predictive cues without punishment, a process called memory extinction. Here, we show that extinction of aversive memories in Drosophila requires specific dopaminergic neurons, which indicate that omission of punishment is remembered as a positive experience. Functional imaging revealed co-existence of intracellular calcium traces in different places in the mushroom body output neuron network for both the original aversive memory and a new appetitive extinction memory. Light and ultrastructural anatomy are consistent with parallel competing memories being combined within mushroom body output neurons that direct avoidance. Indeed, extinction-evoked plasticity in a pair of these neurons neutralizes the potentiated odor response imposed in the network by aversive learning. Therefore, flies track the accuracy of learned expectations by accumulating and integrating memories of conflicting events.S.W. was funded by a Wellcome Principal Research Fellowship (200846/Z/16/Z), by the Gatsby Charitable Foundation (GAT3237), and by the Bettencourt-Schueller Foundation. J.F. was supported by the DFG (FE 1563/1-1). G.S.X.E.J. was funded by Medical Research Council. D.D.B. funded by HHMI. G.S.X.E.J., D.D.B., and S.W. were funded by a Wellcome Collaborative Award (203261/Z/16/Z)

Associative memory: without a trace

Some transient sensory stimuli can cause prolonged activity in the brain. Trace conditioning experiments can reveal the time over which these lasting representations can be utilized and where they reside

Estimating replicability of classifier learning experiments

Replicability of machine learning experiments measures how likely it is that the outcome of one experiment is repeated when performed with a different randomization of the data. In this paper, we present an estimator of replicability of an experiment that is efficient. More precisely, the estimator is unbiased and has lowest variance in the class of estimators formed by a linear combination of outcomes of experiments on a given data set. We gathered empirical data for comparing experiments consisting of different sampling schemes and hypothesis tests. Both factors are shown to have an impact on replicability of experiments. The data suggests that sign tests should not be used due to low replicability. Ranked sum tests show better performance, but the combination of a sorted runs sampling scheme with a t-test gives the most desirable performance judged on Type I and II error and replicability

Un preprocesseur parallele pour le logiciel de mecanique des fluides HPCN3S

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : 26165 F, issue : a.1997 n.48 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Fiber based sensors for the monitoring of liquid Nitrogen levels

Fiber based sensors for the monitoring of liquid Nitrogen levels

Study of Earth and Jupiter-like plasmas for atmospheric entries using a non-transferred arc torch

International audienceThis paper presents the results obtained by a 100 kW non-transferred arc plasma torch dedicated to the studies of plasmas characteristics of atmospheric entries of spatial probes, especially Earth and Jupiter entries. Spectra acquisition of the produced plasmas is achieved using optical emission spectroscopy. For Earth entry conditions, air plasma was obtained with a maximal temperature around 6800 K with a good agreement using atomic lines of oxygen and nitrogen (and also copper coming from electrode's ablation) and molecular bands of N2, CN and $\text{N}_{2}^{+}$ , testifying to a good thermal equilibrium. As the first step in the study of Jupiter atmospheric entry, pure helium plasma was produced with the same maximal temperature of about 7500 K. Helium plasma was achieved for the first time using the plasma torch. Recorded spectra show a continuum, He I lines as well as copper. He II lines are not detected

Differential coding of absolute and relative aversive value in the Drosophila brain

International audienceAnimals use prior experience to assign absolute (good or bad) and relative (better or worse) value to new experience. These learned values guide appropriate later decision making. Even though our understanding of how the valuation system computes absolute value is relatively advanced, the mechanistic underpinnings of relative valuation are unclear. Here, we uncover mechanisms of absolute and relative aversive valuation in Drosophila. Three types of punishment-sensitive dopaminergic neurons (DANs) respond differently to electric shock intensity. During learning, these punishment-sensitive DANs drive intensity-scaled plasticity at their respective mushroom body output neuron (MBON) connections to code absolute aversive value. In contrast, by comparing the absolute value of current and previous aversive experiences, the MBON-DAN network can code relative aversive value by using specific punishment-sensitive DANs and recruiting a specific subtype of reward-coding DANs. Behavioral and physiological experiments revealed that a specific subtype of reward-coding DAN assigns a "better than" value to the lesser of the two aversive experiences. This study therefore highlights how appetitive-aversive system interactions within the MB network can code and compare sequential aversive experiences to learn relative aversive value