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.
Highlights
To demonstrate the power of this approach we show that our model is capable of predicting future motor commands on a cycle-by-cycle basis and is valid across multiple individual C. elegans despite consistent inter-individual differences in neuronal activation
Using imaging of a limited subset of neurons in freely moving C. elegans, Kato et al (2015) verify that activation of some individual neurons is closely associated with parameters of locomotion
We apply this method to neuronal imaging in C. elegans to demonstrate its success in simulating activity of the nervous system and predicting switches between different motor commands
Summary
Advances in neuronal imaging (Kato et al, 2015; Ahrens et al, 2013; Berenyi et al, 2014; Jorgenson et al, 2015; Venkatachalam et al, 2016; Nguyen et al, 2016; Schrodel et al, 2013) are making it possible to simultaneously record activity in a large number of neurons simultaneously during execution of behaviors. Most analytic techniques used to simplify such complex datasets involve dimensionality reduction (Kato et al, 2015), clustering (Venkatachalam et al, 2016), correlations between activity of neuronal populations and behavior (Georgopoulos et al, 1986) or features of the sensory stimuli (Luo et al, 2014), and connectivity among neurons (Varshney et al, 2011). Attempts have been made to model more complex neural networks such as a cortical column at the level of biophysical properties of individual neurons (Markram, 2006; Markram et al, 2015). These modeling approaches can prove successful in some settings, the bottom-up
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