Abstract

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using.

Highlights

  • In recent years, connectomics – the assembly and analysis of comprehensive maps of neural connectivity – has been growing by leaps and bounds

  • The translational gradient does not influence the turning bias on average, when we studied the different cases more systematically we found some information in the translational direction: the magnitude of the turns were larger for negative translational gradients than for positive translational gradients (gray points, Chemotaxis index (CI) Reliability (%)

  • The magnitudes of the corrections are larger when the worm is heading away from the peak than when the worm is heading towards the peak

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Summary

Introduction

Connectomics – the assembly and analysis of comprehensive maps of neural connectivity – has been growing by leaps and bounds. In addition to the experimental assembly of connectome data, there has been a growing interest in studying the large-scale network properties of these connectomes using graph theory [12]– [15]. The focus of this analysis has been on the global properties of the full network, such as small-world, scale-free properties, common motifs, degree distributions, vertex degrees, generalized eccentricities, number of complete subgraphs, clustering structures, etc. Connectomics can provide important insights into the general organizational principles of nervous systems and their impact on neural activity

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