title: A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.
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.
authors: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA. rahuls@mit.edu
authors: Benjamin I
authors: BI
authors: Rapoport
authors: Woradorn
authors: W
authors: Wattanapanitch
authors: Hector L
authors: HL
authors: Penagos
authors: Sam
authors: S
authors: Musallam
authors: Richard A
authors: RA
authors: Andersen
authors: Rahul
authors: R
authors: Sarpeshkar
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D032701:Biomimetics; D001921:Brain / Q000473:pathology; D004867:Equipment Design; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net; D009474:Neurons / Q000473:pathology; D051381:Rats; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013686:Telemetry / Q000295:instrumentation; D013997:Time Factors; D014584:User-Computer Interface