From cortical neural spike trains to behavior: Modeling and analysis.

摘要

Brain machine interface (BMI) design can be achieved by training linear and nonlinear models with simultaneously recorded cortical neural activity and goal directed behavior. Real-time implementation of this technology requires reliable and accurate signal processing models that produce small error variance in the estimated kinematic trajectories. In this dissertation, the mapping performance and generalization of a recurrent multilayer perceptron (RMLP) is compared with standard linear and nonlinear signal processing models for two species of primates and two behavioral tasks. Each modeling approach is shown to have strengths and weaknesses that are compared experimentally. The RMLP approach shows very accurate peak amplitude estimations with small error variance using a parsimonious model topology. To validate and advance the state-of-the-art of this BMI modeling design, it is necessary to understand how the proposed model represents the neural-to-motor mappings. The RMLP is analyzed here and an interpretation of the neural-to-motor solution of this network is built by tracing the signals through the topology using signal processing concepts. We then propose the use of optimized BMI models for analyzing neural activity to assess the role of and importance of individual neurons and cortical areas in generating the performed movement. It is further shown that by pruning the initial ensemble of neural inputs with the ranked importance of cells, a reduced set of cells can be found that exceed the BMI performance levels of the full ensemble.