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Encoding and decoding of kinematic primitives in motor cortex
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Encoding and decoding of kinematic primitives in motor cortex
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Encoding and decoding of kinematic primitives in motor cortex
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Dissertation
Encoding and decoding of kinematic primitives in motor cortex
2011
Overview
We hypothesize that neural activity recorded from the motor cortex, rather than encoding the directly observed motion itself, encodes a sequence of unobserved yet inferable motor primitives that are composed to build the observed motion. We make use of a double-step or \"target jump\" paradigm during 2D planar reaching to reliably induce corrections and decompose the motion during a jump into a primary primitive and a corrective, secondary primitive. We then show that a traditional encoding model, fit to unperturbed trials, does not adequately describe neural data during a jump. Instead, neural activity is better described by first applying the same encoding model to the primary primitive, and then implementing an instantaneous switch to the corrective, secondary primitive. While previous literature has argued for the existence of motor primitives on the basis of psychophysics, this is one of the first demonstrations of a signature of motor primitives in motor cortical activity. We also propose a primitive-based decoding algorithm for use in a brain-machine interface (BMI), whereby a collection of parameterized kinematic primitives are used to represent possible submovements. We define a likelihood ratio statistic, which is the ratio of the probability of movement to the probability of a hold condition. Using data recorded from primary motor cortex of a rhesus macaque, we show that the likelihood ratio can be used to decode the start time of a submovement and the number of submovements, which is difficult to do using existing methods. By linearly summing the most likely submovements, the likelihood ratio can be used to generate an estimate of hand position which is biologically realistic. We hypothesize that such a primitive-based decoding approach will also deliver superior performance during real-time, online neural control.
