Infrared spectra of the isolated protonated flavin molecules lumichrome, lumiflavin, riboflavin (vitamin B2), and the biologically important cofactor flavin mononucleotide are measured in the fingerprint region (600-1850 cm(-1)) by means of IR multiple-photon dissociation (IRMPD) spectroscopy. Using density functional theory calculations, the geometries, relative energies, and linear IR absorption spectra of several low-energy isomers are calculated. Comparison of the calculated IR spectra with the measured IRMPD spectra reveals that the N10 substituent on the isoalloxazine ring influences the protonation site of the flavin. Lumichrome, with a hydrogen substituent, is only stable as the N1-protonated tautomer and protonates at N5 of the pyrazine ring. The presence of the ribityl unit in riboflavin leads to protonation at N1 of the pyrimidinedione moiety, and methyl substitution in lumiflavin stabilizes the tautomer that is protonated at O2. In contrast, flavin mononucleotide exists as both the O2- and N1-protonated tautomers. The frequencies and relative intensities of the two C=O stretch vibrations in protonated flavins serve as reliable indicators for their protonation site.

Foraging animals have to evaluate, compare and select food patches in order to increase their fitness. Understanding what drives foraging decisions requires careful manipulation of the value of alternative options while monitoring animals choices. Value-based decisionmaking tasks in combination with formal learning models have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is despite the fact that fruit flies have served as model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we also show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of reward foraging decisions.

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10Animals often navigate environments that are uncertain, volatile and com-11 plex, making it challenging to locate reliable food sources. Therefore, it is 12 not surprising that many species evolved multiple, parallel and complementary 13 foraging strategies to survive. Current research on animal behavior is largely 14 driven by a reductionist approach and attempts to study one particular aspect 15 of behavior in isolation. This is justified by the huge success of past and current 16 research in understanding neural circuit mechanisms of behaviors. But focus-17 ing on only one aspect of behaviors obscures their inherent multidimensional 18 nature. To fill this gap we aimed to identify and characterize distinct behavioral 19 modules using a simple reward foraging assay. For this we developed a single-20 animal, trial-based probabilistic foraging task, where freely walking fruit flies 21 experience optogenetic sugar-receptor neuron stimulation. By carefully analyz- 22 ing the walking trajectories of flies, we were able to dissect the animals foraging 23 decisions into multiple underlying systems. We show that flies perform local 24 searches, cue-based navigation and learn task relevant contingencies. Using 25 probabilistic reward delivery allowed us to bid several competing reinforcement 26 learning (RL) models against each other. We discover that flies accumulate 27 chosen option values, forget unchosen option values and seek novelty. We 28 further show that distinct behavioral modules -learning and navigation-based 29 systems-cooperate, suggesting that reinforcement learning in flies operates 30 on dimensionality reduced representations. We therefore argue that animals 31 will apply combinations of multiple behavioral strategies to generate foraging 32 decisions.Modular organization of biological systems provides species with flexibility to inde- 35 pendently evolve distinct biological functions [1]. Hierarchical organization on the 36 other hand enables coordination of multiple functions to serve common goals. Seven 37 decades ago Nikolaas Tinbergen has recognized that animal behaviors show ample ev-38 idence of modularity and hierarchy [2]. Both experimental and theoretical work have 39 addressed why and under what conditions distinct behavioral modules or strategies 40 may emerge. 41 Spatial and temporal variations, changes in both mean and variance of quality 42 and quantity of food patches, pose a serious challenge to all foraging animals to 43 optimize decisions. Successful strategies must therefore be shaped to accommodate 44 environmental uncertainty and volatility. A large body of evidence suggests that 45 animals and humans are able to track such changes in the environment [3, 4]. In 46 theory, animals could optimize food gathering performance by using trial and error 47 and incorporate simple forms of reinforcement learning (RL) [5] to deal with variability 48 in their habitats. Indeed, the RL framework has been successfully used to explain 49 animal behavior in many learning paradigms [6]. T...