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Movements during sleep reveal the developmental emergence of a cerebellar-dependent internal model in motor thalamus

Cite this dataset

Dooley, James; Sokoloff, Greta; Blumberg, Mark (2021). Movements during sleep reveal the developmental emergence of a cerebellar-dependent internal model in motor thalamus [Dataset]. Dryad.


With our eyes closed, we can track a limb’s moment-to-moment location in space. If this capacity relied on sensory feedback from the limb, we would always be a step behind because sensory feedback takes time: For the execution of rapid and precise movements, such lags are not tolerable. Nervous systems solve this problem by computing representations—or internal models—that mimic movements as they are happening, with the associated neural activity occurring after the motor command but before the sensory feedback. Research in adults indicates that the cerebellum is necessary to compute internal models. What is not known, however, is when—and under what conditions—this computational capacity develops. Here, taking advantage of the unique kinematic features of the discrete, spontaneous limb twitches that characterize active sleep, we captured the developmental emergence of a cerebellar-dependent internal model. Using rats at postnatal days (P) 12, P16, and P20, we compared neural activity in the ventral posterior (VP) and ventral lateral (VL) thalamic nuclei, both of which receive somatosensory input but only the latter of which receives cerebellar input. At all ages, twitch-related activity in VP lagged behind the movement, consistent with sensory processing; similar activity was observed in VL through P16. At P20, however, VL activity no longer lagged behind movement, but instead precisely mimicked the movement itself; this activity depended on cerebellar input. In addition to demonstrating the emergence of internal models of movement, these findings implicate twitches in their development and calibration through, at least, the preweanling period.


This data consists of electrophysiological spiking data, timecodes for twitches and wake movements, as well as displacement from the video files.

Electrophysioloical data was collected from a TDT LabRat using NeuroNexus electrodes. Spikesorting was done using Kilosort 2, with manual sorting done in phy. Behavioral data was recorded using video collected at ~100 fps, which was then syncronized to the electrophysiological data.

Any additional data (e.g. Video files, LFP) can be requested by the corresponding author of the manuscript (, however the full dataset is terabites of data.

Usage notes

See README.txt


National Cancer Institute, Award: R37-HD081168

National Cancer Institute, Award: F32-NS101858