How do animals adaptively integrate innate with learned behavioral tendencies? We tackle this question using chemotaxis as a paradigm. Chemotaxis in the Drosophila larva largely results from a sequence of runs and oriented turns. Thus, the larvae minimally need to determine (i) how fast to run, (ii) when to initiate a turn, and (iii) where to direct a turn. We first report how odor-source intensities modulate these decisions to bring about higher levels of chemotactic performance for higher odor-source intensities during innate chemotaxis. We then examine whether the same modulations are responsible for alterations of chemotactic performance by learned odor "valence" (understood throughout as level of attractiveness). We find that run speed (i) is neither modulated by the innate nor by the learned valence of an odor. Turn rate (ii), however, is modulated by both: the higher the innate or learned valence of the odor, the less often larvae turn whenever heading toward the odor source, and the more often they turn when heading away. Likewise, turning direction (iii) is modulated concordantly by innate and learned valence: turning is biased more strongly toward the odor source when either innate or learned valence is high. Using numerical simulations, we show that a modulation of both turn rate and of turning direction is sufficient to account for the empirically found differences in preference scores across experimental conditions. Our results suggest that innate and learned valence organize adaptive olfactory search behavior by their summed effects on turn rate and turning direction, but not on run speed. This work should aid studies into the neural mechanisms by which memory impacts specific aspects of behavior.

We received project support by the European Commission (FP7-ICT project Miniature Insect Model for Active Learning [MINIMAL]). M.L. and S.F.R. acknowledge further funding from the Spanish Ministry of Science and Innovation (MICINN, BFU2011-483 26208) and the EMBL/CRG Systems Biology Program. A.D. acknowledges support via the grants EP/F500385/1 and BB/F529254/1 for the University of Edinburgh School of Informatics Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, the UK Engineering and Physical Sciences Research Council (EPSRC), UK Biotechnology and Biological Sciences Research Council (BBSRC), and the UK Medical Research Council (MRC). B.G. received additional support from the Deutsche Forschungsgemeinschaft (DFG) (SFB 779 Motivated behavior), and the Bundesministerium fu¨r Bildung und Forschung (BMBF) (Bernstein Network Insect inspired robotics).