publications

2022

1. Flexible neural control of motor units

   Najja J. Marshall, Joshua I. Glaser, Eric M. Trautmann, and 6 more authors

   Nature Neuroscience, 2022

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   Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Henneman’s size principle. Although this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. In this study, we developed an isometric task that allowed stable MU recordings, in a rhesus macaque, even during rapidly changing forces. Patterns of MU activity were surprisingly behavior-dependent and could be accurately described only by assuming multiple drives. Consistent with flexible descending control, microstimulation of neighboring cortical sites recruited different MUs. Furthermore, the cortical population response displayed sufficient degrees of freedom to potentially exert fine-grained control. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability. Muscle fibers have diverse properties—for example, slow and fast twitch. Groups of fibers are activated by motoneurons. Marshall et al. found that motoneurons are used flexibly, presumably allowing us to intelligently employ fibers suited to each task.

2021

1. Connectivity patterns shape sensory representation in a cerebellum-like network

   Daniel Zavitz, Elom A. Amematsro, Alla Borisyuk, and 1 more author

   bioRxiv, 2021

   Abs

   Cerebellum-like structures are found in many brains and share a basic fan-out–fan-in network architecture. How the specific structural features of these networks give rise to their learning function remains largely unknown. To investigate this structure–function relationship, we developed a realistic computational model of an empirically very well-characterized cerebellum-like structure, the Drosophila melanogaster mushroom body. We show how well-defined connectivity patterns between the Kenyon cells, the constituent neurons of the mushroom body, and their input projection neurons enable different functions. First, biases in the likelihoods at which individual projection neurons connect to Kenyon cells allow the mushroom body to prioritize the learning of particular, ethologically meaningful odors. Second, groups of projection neurons connecting preferentially to the same Kenyon cells facilitate the mushroom body generalizing across similar odors. Altogether, our results demonstrate how different connectivity patterns shape the representation space of a cerebellum-like network and impact its learning outcomes.