A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders

A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders. Rapoport, B., Wattanapanitch, W., Penagos, H., Musallam, S., Andersen, R. A., & Sarpeshkar, R. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 4214 -4217, sept., 2009.

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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.

@INPROCEEDINGS{5333793,
author={Rapoport, B.I. and Wattanapanitch, W. and Penagos, H.L. and Musallam, Sam and Andersen, R. A. and Sarpeshkar, R.},
booktitle={Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE},
title={A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders},
year={2009},
month={sept.},
volume={},
number={},
pages={4214 -4217},
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.},
keywords={adaptive linear filters;biomimetic adaptive algorithm;continuous-time artificial neural network;data compression;direct control signals;implantable brain-machine interface;implantable neural decoders;low-power architecture;micropower analog circuit architecture;neural cell ensemble signal decoding;on-line learning process;real time system;synaptic dynamics;thalamic head-direction cells;wireless data transmission;adaptive filters;biomedical electronics;biomimetics;brain;brain-computer interfaces;data compression;decoding;medical signal processing;neural nets;neurophysiology;prosthetics;real-time systems;wireless channels;Algorithms;Animals;Bayes Theorem;Biomimetics;Brain;Equipment Design;Models, Neurological;Models, Statistical;Nerve Net;Neurons;Rats;Signal Processing, Computer-Assisted;Telemetry;Time Factors;User-Computer Interface;},
doi={10.1109/IEMBS.2009.5333793},
ISSN={1557-170X},
URL = {http://npl.mcgill.ca/Papers/biomimetic%20EEE_EMBS_2009.pdf},
}

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