Chemoanatomical organization of the noradrenergic input from locus coeruleus to the olfactory bulb of the adult rat.

The locus coeruleus contains noradrenergic neurons which project widely throughout the CNS. A major target of locus coeruleus projections in the rat is the olfactory bulb (Shipley et al.: Brain Res. 329:294–299, '85) but the organization of the projections within the bulb has not been systematically examined. In this study, the laminar distribution and densities of locus coeruleus-noradrenergic fibers in the main and accessory olfactory bulbs were determined with anterograde tracing and immunocytochemical techniques. Following iontophoretic injections of 1% wheat germ agglutinin-horseradish peroxidase into the locus coeruleus, the densest anterograde label in the accessory olfactory bulb was observed in the external plexiform layer, granule cell layer, and especially in the internal part of the mitral cell layer. Virtually no label was observed in the glomerular layer. In the main olfactory bulb, labelled axons were observed in the granule cell layer, in the internal and external plexiform layers, occasionally in the mitral cell layer, and least often in the glomerular layer. Noradrenergic fibers in the olfactory bulb were identified by using immunocytochemistry with an antibody to dopamine-β-hydroxylase. Laminar patterns and densities of noradrenergic innervation were determined with quantitative image analysis. In the accessory olfactory bulb, the densest innervation was in the innermost portion of the mitral cell layer followed by the granule cell layer, the superficial part of the mitral cell layer, and the external plexiform layer. The density of fibers in the glomerular layer was least. The laminar pattern of noradrenergic fiber distribution in the main olfactory bulb was similar to that in accessory olfactory bulb. The present studies demonstrate that locus coeruleus-noradrenergic fibers terminate preferentially in the internal plexiform, granule cell, and external plexiform layers. This suggests that the major influence of the locus coeruleus input to both the main and accessory the olfactory bulbs is on the predominant neuronal element in those layers, the granule cells. Additional studies are needed to resolve how this input influences specific olfactory bulb circuits

The geometry of spontaneous spiking in neuronal networks

The mathematical theory of pattern formation in electrically coupled networks of excitable neurons forced by small noise is presented in this work. Using the Freidlin-Wentzell large deviation theory for randomly perturbed dynamical systems and the elements of the algebraic graph theory, we identify and analyze the main regimes in the network dynamics in terms of the key control parameters: excitability, coupling strength, and network topology. The analysis reveals the geometry of spontaneous dynamics in electrically coupled network. Specifically, we show that the location of the minima of a certain continuous function on the surface of the unit n-cube encodes the most likely activity patterns generated by the network. By studying how the minima of this function evolve under the variation of the coupling strength, we describe the principal transformations in the network dynamics. The minimization problem is also used for the quantitative description of the main dynamical regimes and transitions between them. In particular, for the weak and strong coupling regimes, we present asymptotic formulas for the network activity rate as a function of the coupling strength and the degree of the network. The variational analysis is complemented by the stability analysis of the synchronous state in the strong coupling regime. The stability estimates reveal the contribution of the network connectivity and the properties of the cycle subspace associated with the graph of the network to its synchronization properties. This work is motivated by the experimental and modeling studies of the ensemble of neurons in the Locus Coeruleus, a nucleus in the brainstem involved in the regulation of cognitive performance and behavior

Humans and Insects Decide in Similar Ways

Behavioral ecologists assume that animals use a motivational mechanism for decisions such as action selection and time allocation, allowing the maximization of their fitness. They consider both the proximate and ultimate causes of behavior in order to understand this type of decision-making in animals. Experimental psychologists and neuroeconomists also study how agents make decisions but they consider the proximate causes of the behavior. In the case of patch-leaving, motivation-based decision-making remains simple speculation. In contrast to other animals, human beings can assess and evaluate their own motivation by an introspection process. It is then possible to study the declared motivation of humans during decision-making and discuss the mechanism used as well as its evolutionary significance. In this study, we combine both the proximate and ultimate causes of behavior for a better understanding of the human decision-making process. We show for the first time ever that human subjects use a motivational mechanism similar to small insects such as parasitoids [1] and bumblebees [2] to decide when to leave a patch. This result is relevant for behavioral ecologists as it supports the biological realism of this mechanism. Humans seem to use a motivational mechanism of decision making known to be adaptive to a heterogeneously distributed resource. As hypothesized by Hutchinson et al. [3] and Wilke and Todd [4], our results are consistent with the evolutionary shaping of decision making because hominoids were hunters and gatherers on food patches for more than two million years. We discuss the plausibility of a neural basis for the motivation mechanism highlighted here, bridging the gap between behavioral ecology and neuroeconomy. Thus, both the motivational mechanism observed here and the neuroeconomy findings are most likely adaptations that were selected for during ancestral times

Shadowing in the nuclear photoabsorption above the resonance region

A model based on the hadronic fluctuations of the real photon is developed to describe the total photonucleon and photonuclear cross sections in the energy region above the nucleon resonances. The hadronic spectral function of the photon is derived including the finite width of vector-meson resonances and the quark-antiquark continuum. The shadowing effect is evaluated considering the effective interaction of the hadronic component with the bound nucleons within a Glauber-Gribov multiple scattering theory. The low energy onset of the shadowing effect is interpreted as a possible signature of a modification of the hadronic spectral function in the nuclear medium. A decrease of the $\rho$-meson mass in nuclei is suggested for a better explanation of the experimental data.Comment: 8 pages, 7 figure

Individual differences in infant oculomotor behavior during the viewing of complex naturalistic scenes

Little research hitherto has examined how individual differences in attention, as assessed using standard experimental paradigms, relate to individual differences in how attention is spontaneously allocated in more naturalistic contexts. Here, we analyzed the time intervals between refoveating eye movements (fixation durations) while typically developing 11-month-old infants viewed a 90-min battery ranging from complex dynamic to noncomplex static materials. The same infants also completed experimental assessments of cognitive control, psychomotor reaction times (RT), processing speed (indexed via peak look during habituation), and arousal (indexed via tonic pupil size). High test–retest reliability was found for fixation duration, across testing sessions and across types of viewing material. Increased cognitive control and increased arousal were associated with reduced variability in fixation duration. For fixations to dynamic stimuli, in which a large proportion of saccades may be exogenously cued, we found that psychomotor RT measures were most predictive of mean fixation duration; for fixations to static stimuli, in contrast, in which there is less exogenous attentional capture, we found that psychomotor RT did not predict performance, but that measures of cognitive control and arousal did. The implications of these findings for understanding the development of attentional control in naturalistic settings are discussed

The Wave Function of 2S Radially Excited Vector Mesons from Data for Diffraction Slope

In the color dipole gBFKL dynamics we predict a strikingly different Q^2 and energy dependence of the diffraction slope for the elastic production of ground state V(1S) and radially excited V'(2S) light vector mesons. The color dipole model predictions for the diffraction slope for \rho^0 and \phi^0 production are in a good agreement with the data from the fixed target and collider HERA experiments. We present how a different form of anomalous energy and Q^2 dependence of the diffraction slope for V'(2S) production leads to a different position of the node in radial wave function and discuss a possibility how to determine this position from the fixed target and HERA data.Comment: 20 pages and 6 figures. Title change

Assessing Levels of Attention Using Low Cost Eye Tracking

The emergence of mobile eye trackers embedded in next generation smartphones or VR displays will make it possible to trace not only what objects we look at but also the level of attention in a given situation. Exploring whether we can quantify the engagement of a user interacting with a laptop, we apply mobile eye tracking in an in-depth study over 2 weeks with nearly 10.000 observations to assess pupil size changes, related to attentional aspects of alertness, orientation and conflict resolution. Visually presenting conflicting cues and targets we hypothesize that it's feasible to measure the allocated effort when responding to confusing stimuli. Although such experiments are normally carried out in a lab, we are able to differentiate between sustained alertness and complex decision making even with low cost eye tracking "in the wild". From a quantified self perspective of individual behavioral adaptation, the correlations between the pupil size and the task dependent reaction time and error rates may longer term provide a foundation for modifying smartphone content and interaction to the users perceived level of attention.Comment: 12 pages, 6 figures, 2 tables. The final publication will be available at Springer via http://dx.doi.org/DOIxxx, when published as part of the HCI International 2016 Conference Proceeding

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating