A biomimetic adaptive algorithm and low-power architecture for decoders
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Rapoport-2009-A biomimetic adaptiv.pdf
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Author(s)
Andersen, Richard A.
Musallam, Sam
Penagos, Hector L.
Wattanapanitch, Woradorn
Rapoport, Benjamin I.
Sarpeshkar, Rahul
Date Issued
November 2009
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
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.
Version
Final published version
Abstract
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.
Subjects
adaptive algorithms
low-power
neural decoding
brain-machine interface
biomimetic
analog
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/IEMBS.2009.5333793
