bioRxivBiophysicsPreprint
Multi-dimensional structure of C. elegans thermal learning
Abstract
Quantitative models of associative learning that explain behavior of real animals with high precision have turned out very difficult to construct. We do this in the context of the dynamics of the thermal preference of C. elegans. For this, we quantify C. elegans thermotaxis in response to various conditioning parameters, genetic perturbations, and operant behavior using a fast, high-throughput microfluidic droplet assay. We then model this data comprehensively, within a new, biologically interpretable, multi-modal framework. We discover that the dynamics of thermal preference are described by two independent contributions and require a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates to the temperature when food is absent.
- organism: C. elegans
- assay: behavioral assay
- research focus: cell type annotation
- approach: deep learning
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