Internal models for interpreting neural population activity during sensorimotor control
- Carnegie Mellon University, United States
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.
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Author details
Reviewing Editor
- Timothy Behrens, Oxford University, United Kingdom
Ethics
Animal experimentation: All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Pittsburgh (protocol 0808279).
Version history
- Received: July 10, 2015
- Accepted: November 25, 2015
- Accepted Manuscript published: December 8, 2015 (version 1)
- Version of Record published: February 16, 2016 (version 2)
Copyright
© 2015, Golub et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Internal models for interpreting neural population activity during sensorimotor control
eLife 4:e10015.
https://doi.org/10.7554/eLife.10015
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