Emergence of long timescales and stereotyped behaviors in Caenorhabditis elegans

Animal behaviors often are decomposable into discrete, stereotyped elements, well separated in time. In one model, such behaviors are triggered by specific commands; in the extreme case, the discreteness of behavior is traced to the discreteness of action potentials in the individual command neurons. Here, we use the crawling behavior of the nematode Caenorhabditis elegans to demonstrate the opposite view, in which discreteness, stereotypy, and long timescales emerge from the collective dynamics of the behavior itself. In previous work, we found that as C. elegans crawls, its body moves through a “shape space” in which four dimensions capture approximately 95% of the variance in body shape. Here we show that stochastic dynamics within this shape space predicts transitions between attractors corresponding to abrupt reversals in crawling direction. With no free parameters, our inferred stochastic dynamical system generates reversal timescales and stereotyped trajectories in close agreement with experimental observations. We use the stochastic dynamics to show that the noise amplitude decreases systematically with increasing time away from food, resulting in longer bouts of forward crawling and suggesting that worms can use noise to modify their locomotory behavior.

[1]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[2]  P. N. Kugler,et al.  Patterns of human interlimb coordination emerge from the properties of non-linear, limit cycle oscillatory processes: theory and data. , 1981, Journal of motor behavior.

[3]  S. Brenner,et al.  The neural circuit for touch sensitivity in Caenorhabditis elegans , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  James P. Crutchfield,et al.  Equations of Motion from a Data Series , 1987, Complex Syst..

[5]  P. Hänggi,et al.  Reaction-rate theory: fifty years after Kramers , 1990 .

[6]  I. Stewart,et al.  Coupled nonlinear oscillators and the symmetries of animal gaits , 1993 .

[7]  Mark Dykman,et al.  Large fluctuations and optimal paths in chemical kinetics , 1994 .

[8]  Cori Bargmann,et al.  Signal transduction in the Caenorhabditis elegans nervous system. , 1998, Annual review of neuroscience.

[9]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[10]  Ryuzo Shingai,et al.  Durations and frequencies of free locomotion in wild type and GABAergic mutants of Caenorhabditis elegans , 2000, Neuroscience Research.

[11]  Rajesh Ranganathan,et al.  C. elegans Locomotory Rate Is Modulated by the Environment through a Dopaminergic Pathway and by Experience through a Serotonergic Pathway , 2000, Neuron.

[12]  Friedrich,et al.  How to quantify deterministic and random influences on the statistics of the foreign exchange market , 1999, Physical review letters.

[13]  P. Sternberg,et al.  Goalpha regulates volatile anesthetic action in Caenorhabditis elegans. , 2001, Genetics.

[14]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[15]  J. Zinn-Justin Quantum Field Theory and Critical Phenomena , 2002 .

[16]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[17]  Beibei Zhao,et al.  Reversal Frequency in Caenorhabditis elegans Represents an Integrated Response to the State of the Animal and Its Environment , 2003, The Journal of Neuroscience.

[18]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[19]  A. Porporato,et al.  Langevin equations from time series. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Cori Bargmann,et al.  A circuit for navigation in Caenorhabditis elegans , 2005 .

[21]  D. Faber,et al.  The Mauthner Cell Half a Century Later: A Neurobiological Model for Decision-Making? , 2005, Neuron.

[22]  W. Bialek,et al.  A sensory source for motor variation , 2005, Nature.

[23]  Andreas Daffertshofer,et al.  Deterministic and stochastic features of rhythmic human movement , 2006, Biological Cybernetics.

[24]  Bruce R. Johnson An Introduction to Nervous Systems , 2007, Journal of Undergraduate Neuroscience Education.

[25]  Cori Bargmann,et al.  Microfluidics for in vivo imaging of neuronal and behavioral activity in Caenorhabditis elegans , 2007, Nature Methods.

[26]  Greg J. Stephens,et al.  Dimensionality and Dynamics in the Behavior of C. elegans , 2007, PLoS Comput. Biol..

[27]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[28]  N. A. Croll Components and patterns in the behaviour of the nematode Caenorhabditis elegans , 2009 .

[29]  Greg J. Stephens,et al.  From Modes to Movement in the Behavior of Caenorhabditis elegans , 2009, PloS one.

[30]  Leonardo Lancia,et al.  Nonlinear dynamics in speech perception , 2010 .