Abstract
A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps to reveal the brain's underlying computations. We investigate how the nematode Caenorhabditis elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. We find that the behavioral response is tuned to temporal properties of mechanosensory signals, such as their integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal's response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal's behavioral response to stimulus.
Highlights
An animal’s nervous system interprets sensory signals to guide behavior, including behaviors that are involved in evading predation
We find that the animal responds to the temporal features of signals in its mechanosensory neurons, such as its time-derivative, that extend over many seconds
That we find evidence of temporal processing and context dependency, even in the nematode’s relatively simple touch circuit, raises the possibility that these features could be ubiquitous across sensory systems
Summary
An animal’s nervous system interprets sensory signals to guide behavior, including behaviors that are involved in evading predation. The behavioral response to dynamic time-varying mechanosensory signals has not been fully explored. We provide new details about the mechanosensory response system by quantitatively exploring the animal’s detailed behavioral response to rich, dynamically varying signals. We find that the animal responds to the temporal features of signals in its mechanosensory neurons, such as its time-derivative (i.e. rate of change), that extend over many seconds. That we find evidence of temporal processing and context dependency, even in the nematode’s relatively simple touch circuit, raises the possibility that these features could be ubiquitous across sensory systems. We present a simple quantitative model that predicts the animal’s response to novel mechanosensory signals
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