Abstract
The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.
Highlights
Even in the absence of environmental cues, neurons receive and produce a barrage of fluctuating, ongoing signals
To shed light on the influence of activity fluctuations on action timing, we developed a computational approach for automatically generating neural network models that reproduce large-scale, high-resolution behavioral measurements of freely walking Drosophila melanogaster
The dynamical features that make central circuits more or less susceptible to the influence of activity fluctuations are unknown. These may include the location and number of stable and unstable equilibrium points in neural activity phase space. To address this question we developed a method for automatically generating neural network models that reproduce measured animal behaviors
Summary
Even in the absence of environmental cues, neurons receive and produce a barrage of fluctuating, ongoing signals. These fluctuations are both deterministic, reflecting a neuron’s embedding within complex dynamical networks, and random, arising from stochastic noise sources at synapses and ion channels [1,2]. Action selection (AS) circuits [8], including ‘command’ neurons that drive behavior from moment to moment [9,10,11], may be susceptible to activity fluctuations: they represent information bottlenecks where a relatively small number of neurons can have a disproportionately large influence on actions. Behavioral transitions in C. elegans can be effectively captured using a tunable stochastic term within a deterministic mathematical framework [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
