Brains regulate behavioral responses with distinct timings. Here we investigate the cellular and molecular mechanisms underlying the timing of decision-making during olfactory navigation in Caenorhabditis elegans. We find that, based on subtle changes in odor concentrations, the animals appear to choose the appropriate migratory direction from multiple trials as a form of behavioral decision-making. Through optophysiological, mathematical and genetic analyses of neural activity under virtual odor gradients, we further find that odor concentration information is temporally integrated for a decision by a gradual increase in intracellular calcium concentration ([Ca 2+ ] i ), which occurs via L-type voltage-gated calcium channels in a pair of olfactory neurons. In contrast, for a reflex-like behavioral response, [Ca 2+ ] i rapidly increases via multiple types of calcium channels in a pair of nociceptive neurons. Thus, the timing of neuronal responses is determined by cell type-dependent involvement of calcium channels, which may serve as a cellular basis for decision-making.

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Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach for behavioral st ate e stimation and f eature ex tr action (STEFTR) only from low-content animal trajectory data. To demonstrate the effectiveness of the proposed method, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales—from mm to 1,000 km in space and from sub-seconds to days in time. We successfully estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.

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Classification: Biological Sciences, Neuroscience 24 25 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198879 doi: bioRxiv preprint first posted online Oct. 9, 2017; ABSTRACT 26Animal behavior is the integrated output of multiple brain functions. However, 27 understanding how multiple brain functions affect behavior has been difficult. In order to 28 decipher dynamic brain functions from time-series of behavioral data, we developed a 29 machine learning strategy that extracts distinguishing behavioral features of sensory 30 navigation. We first investigated experience-dependent enhancement of odor avoidance 31 behavior of the nematode Caenorhabditis elegans. We segmented worms' trajectories 32 during olfactory navigation into two behavioral states, analyzed 92 features of the states, 33 and automatically extracted 9 distinguishing features modulated by prior odor 34 experience using a statistical index, the gain ratio. The extracted features included ones 35 previously unidentified, one of which indicated that the prior odor experience lowers 36 worms' behavioral responses to a small increase in odor concentration, causing enhanced 37 odor avoidance. In fact, calcium imaging analysis revealed that the response of ASH 38 nociceptive neurons to a small odor increase was significantly reduced after prior odor 39 experience. In addition, based on extracted features, multiple mutant strains were 40 categorized into several groups that are related to physiological functions of the mutated 41 genes, suggesting a possible estimation of unknown gene function by behavioral features. 42Furthermore, we also extracted behavioral features modulated by experience in acoustic 43 navigation of bats. Thus, our results demonstrate that, regardless of animal species, 44 sensory modality, and spatio-temporal scale, behavioral features during navigation can be 45 extracted by machine learning analysis, which may lead to the understanding of 46 information processing in ...