Many naturalistic behaviors are built from modular components that are expressed sequentially. Although striatal circuits have been implicated in action selection and implementation, the neural mechanisms that compose behavior in unrestrained animals are not well understood. Here, we record bulk and cellular neural activity in the direct and indirect pathways of dorsolateral striatum (DLS) as mice spontaneously express action sequences. These experiments reveal that DLS neurons systematically encode information about the identity and ordering of sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways. Furthermore, lesioning the DLS prevents appropriate sequence assembly during exploratory or odor-evoked behaviors. By characterizing naturalistic behavior at neural timescales, these experiments identify a code for elemental 3D pose dynamics built from complementary pathway dynamics, support a role for DLS in constructing meaningful behavioral sequences, and suggest models for how actions are sculpted over time.

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Highlights d Naturalistic larval zebrafish behavior is observed with a moving camera system d Probabilistic models are used to predict and simulate behavioral sequences d Models combine environmental dynamics, behavioral history, and hunger state d Simulations capture behavioral dynamics spanning multiple timescales

Modern recording techniques enable large-scale measurements of neural activity in a variety of model organisms. The dynamics of neural activity shed light on how organisms process sensory information and generate motor behavior. Here, we study these dynamics using optical recordings of neural activity in the nematode C. elegans. To understand these data, we develop state space models that decompose neural time-series into segments with simple, linear dynamics. We incorporate these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability. This framework reveals latent states of population neural activity, along with the discrete behavioral states that govern dynamics in this state space. We find stochastic transition patterns between discrete states and see that transition probabilities are determined by both current brain activity and sensory cues.Our methods automatically recover transition times that closely match manual labels of different behaviors, such as forward crawling, reversals, and turns. Finally, the resulting model can simulate neural data, faithfully capturing salient patterns of whole brain dynamics seen in real data. Tonic signaling from O 2 sensors sets neural circuit activity and behavioral state. Nature neuroscience, 15(4):581, 2012. M. Chalfie, J. E. Sulston, J. G. White, E. Southgate, J. N. Thomson, and S. Brenner. The neural circuit for touch sensitivity in Caenorhabditis elegans. Journal of Neuroscience, 5(4):956-964, 1985. C.-B. Chang and M. Athans. State estimation for discrete systems with switching parameters. IEEE Transactions on Aerospace and Electronic Systems, AES-14(3):418-425, 1978. Z. Chen, S. N. Gomperts, J. Yamamoto, and M. A. Wilson. Neural representation of spatial topology in the rodent hippocampus. Neural Computation, 26(1):1-39, 2014. Monte Carlo sampling for tuning-curve analysis. J. Neurophysiol., 103(1):591-602, Jan. 2010. J. P. Cunningham and M. B. Yu. Dimensionality reduction for large-scale neural recordings. Nature neuroscience, 17(11):1500, 2014. P. Dayan and L. F. Abbott. Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press, 2001. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm.