Self-organizing model of motor cortical activities during drawing

Siming H. Lin; Jennie Si; Andrew B. Schwartz

doi:10.1117/12.240893

30 May 1996 Self-organizing model of motor cortical activities during drawing

Siming H. Lin, Jennie Si, Andrew B. Schwartz

Proceedings Volume 2718, Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation; (1996) https://doi.org/10.1117/12.240893
Event: 1996 Symposium on Smart Structures and Materials, 1996, San Diego, CA, United States

Abstract

The population vector algorithm has been developed to combine the simultaneous direction- related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In our study, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. Self-organizing feature mapping (SOFM) is used to select the optimal set of weights in the model to determine the contribution of individual neuron to the overall movement. The correspondence between the movement directions and the discharge patterns of the motor cortical neurons is established in the output map. The topology preserving property of the SOFM is used to analyze real recorded data of a behavior monkey. The data used in this analysis were taken while the monkey was drawing spirals and doing the center out movement. Using such a statistical model, the monkey's arm moving directions could be well predicted based on the motor cortex neuronal firing information.

Citation Download Citation

Siming H. Lin, Jennie Si, and Andrew B. Schwartz "Self-organizing model of motor cortical activities during drawing", Proc. SPIE 2718, Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation, (30 May 1996); https://doi.org/10.1117/12.240893

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