000213096 001__ 213096
000213096 005__ 20190416103238.0
000213096 022__ $$a1553-734X
000213096 02470 $$2ISI$$a000365801600035
000213096 0247_ $$2doi$$a10.1371/journal.pcbi.1004577
000213096 037__ $$aARTICLE
000213096 245__ $$aFluctuation-driven neural dynamics reproduce Drosophila locomotor patterns
000213096 269__ $$a2015
000213096 260__ $$c2015
000213096 336__ $$aJournal Articles
000213096 500__ $$aFeatured on the journal cover page.
000213096 520__ $$aThe 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.
000213096 542__ $$fCC BY
000213096 6531_ $$aEvolution
000213096 6531_ $$aEvolutionary Robotics
000213096 700__ $$0244468$$aMaesani, Andrea$$g195419
000213096 700__ $$0245307$$aRamdya, Pavan$$g196838
000213096 700__ $$aCruchet, Steeve
000213096 700__ $$0247577$$aGustafson, Kyle$$g199185
000213096 700__ $$aBenton, Richard
000213096 700__ $$0240742$$aFloreano, Dario$$g111729
000213096 773__ $$j11$$k11$$qe1004577$$tPlos Computational Biology
000213096 8564_ $$s130916$$uhttps://infoscience.epfl.ch/record/213096/files/image.pcbi.v11.i11.g001.PNG.pdf$$yCover page image$$zCover page image
000213096 8564_ $$s3427275$$uhttps://infoscience.epfl.ch/record/213096/files/journal.pcbi.1004577.pdf$$yPublisher's version$$zPublisher's version
000213096 8560_ $$fpierre.devaud@epfl.ch
000213096 909C0 $$0252161$$pLIS$$xU10370
000213096 909CO $$ooai:infoscience.tind.io:213096$$pSTI$$particle$$qGLOBAL_SET
000213096 917Z8 $$x195419
000213096 917Z8 $$x111729
000213096 917Z8 $$x195419
000213096 917Z8 $$x111729
000213096 917Z8 $$x111729
000213096 917Z8 $$x255330
000213096 937__ $$aEPFL-ARTICLE-213096
000213096 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000213096 980__ $$aARTICLE
000213096 999C0 $$0264413$$mkathryn.aitkenmaulaz@epfl.ch$$pUPRAMDYA$$xU13360$$zBlumer, Eliane