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A Biomimetic Adaptive Algorithm and Low-Power Architecture for Implantable Neural Decoders
 

Summary: A Biomimetic Adaptive Algorithm and Low-Power
Architecture for Implantable Neural Decoders
Benjamin I. Rapoport, Student Member, IEEE, Woradorn Wattanapanitch, Student Member, IEEE,
Hector L. Penagos, Sam Musallam, Richard A. Andersen, and Rahul Sarpeshkar, Senior Member, IEEE
Abstract--Algorithmically and energetically efficient computa-
tional 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 wire-
less 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 trans-
form 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.

  

Source: Andersen, Richard - Division of Biology, California Institute of Technology

 

Collections: Biology and Medicine