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Sprague, D. Y.

Publications and source records attributed to Sprague, D. Y..

2 recordsLinked to original sources

Relative phase of distributed oscillatory dynamics implements a working memory in a simple brain

Working memory allows an animal to gather sensory evidence over time, integrate it with evolving internal needs, and make informed decisions about when and how to act. Simple nervous systems enable careful mechanistic dissection of neuronal micro-dynamics underlying putative conserved mechanisms of cognitive function. In this study, we show that the nematode C. elegans makes sensory-guided turns while foraging and can maintain a working memory of sensory activation prior to the execution of a turn. This information is integrated with body posture to localize appetitive stimuli. Using a virtual-reality whole-brain imaging and neural perturbation system, we find that this working memory is implemented by the coupled oscillations of two distributed neural motor command complexes. One complex decouples from motor output after sensory evidence accumulation, exhibits persistent oscillatory dynamics, and initiates turn execution. The second complex serves as a reference timer. We propose that the implementation of working memory via internalization of motor oscillations could represent the evolutionary origin of internal neural processing, i.e. thought, and a foundation of higher cognition.

neuroscience↗

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.

neuroscience↗