Abstract
To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.
Highlights
Even simple movements, like reaching to grasp a glass of water, require dozens of muscles to be activated with precise coordination
The causal relationship between recorded neural activity and brain-machine interface (BMI) cursor movements is completely specified by the experimenter, and can be chosen to be linear
In the BMI, task-relevant sensory feedback is limited to a single modality, which is completely specified by the experimenter
Summary
Like reaching to grasp a glass of water, require dozens of muscles to be activated with precise coordination. This precision is especially impressive in light of sensory feedback delays inherent to neural transmission and processing: when we make a swift arm movement, the brain only knows where the arm was a split second ago, not where it currently is. Mechanistic studies have made important progress toward identifying the neural circuits that implement internal models in sensory (Komatsu, 2006; Kennedy et al, 2014; Schneider et al, 2014), vestibular (Laurens et al, 2013), and motor (Sommer, 2002; Ghasia et al, 2008; Keller and Hahnloser, 2009; Azim et al, 2014) systems. Together with studies showing neural correlates of internal models (Sommer, 2002; Gribble and Scott, 2002; Ghasia et al, 2008; Mulliken et al, 2008; Keller and Hahnloser, 2009; Green and Angelaki, 2010; Berkes et al, 2011; Laurens et al, 2013), these previous studies have provided strong evidence for the brain’s use of internal models
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