A biomimetic adaptive algorithm and low-power architecture for decoders
Author(s)Andersen, Richard A.; Musallam, Sam; Penagos, Hector L.; Wattanapanitch, Woradorn; Rapoport, Benjamin I.; Sarpeshkar, Rahul; ... Show more Show less
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Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009.
Institute of Electrical and Electronics Engineers
Rapoport, B.I. et al. “A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009. 4214-4217. © 2009 Institute of Electrical and Electronics Engineers.
Final published version
INSPEC Accession Number: 10984107
adaptive algorithms, low-power, neural decoding, brain-machine interface, biomimetic, analog