Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Brief report : Academic amotivation in light of the dark side of identity formation

The study intended to determine motivational profiles of first-year undergraduates and aimed their characterization in terms of identity processes. First, a cluster analysis revealed five motivational profiles: combined (i.e., high quantity of motivation, low amotivation); intrinsic (i.e., high intrinsic, low introjected and external regulation, low amotivation); "demotivated" (i.e., very low quantity of motivation and amotivation); extrinsic (i.e., high extrinsic and identified regulation and low intrinsic and amotivation); and "amotivated" (i.e., low intrinsic and identified, very high amotivation). Second, using Lebart's (2000) methodology, the most characteristic identity processes were listed for each motivational cluster. Demotivated and amotivated profiles were refined in terms of adaptive and maladaptive forms of exploration. Notably, exploration in breadth and in depth were underrepresented in demotivated students compared to the total sample; commitment and ruminative exploration were under and overrepresented respectively in amotivated students. Educational and clinical implications are proposedand future research is suggested.