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• W2957024964 abstract "In simple organisms such as Caenorhabditis elegans, whole brain imaging has been performed. Here, we use such recordings to model the nervous system. Our model uses neuronal activity to predict expected time of future motor commands up to 30 s prior to the event. These motor commands control locomotion. Predictions are valid for individuals not used in model construction. The model predicts dwell time statistics, sequences of motor commands and individual neuron activation. To develop this model, we extracted loops spanned by neuronal activity in phase space using novel methodology. The model uses only two variables: the identity of the loop and the phase along it. Current values of these macroscopic variables predict future neuronal activity. Remarkably, our model based on macroscopic variables succeeds despite consistent inter-individual differences in neuronal activation. Thus, our analytical framework reconciles consistent individual differences in neuronal activation with macroscopic dynamics that operate universally across individuals." @default.
• W2957024964 created "2019-07-23" @default.
• W2957024964 creator A5017490736 @default.
• W2957024964 creator A5088680139 @default.
• W2957024964 date "2019-07-11" @default.
• W2957024964 modified "2023-10-18" @default.
• W2957024964 title "A quantitative model of conserved macroscopic dynamics predicts future motor commands" @default.
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• W2957024964 doi "https://doi.org/10.7554/elife.46814" @default.
• W2957024964 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6624016" @default.
• W2957024964 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31294689" @default.
• W2957024964 hasPublicationYear "2019" @default.
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