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The Neural Engine: A Reprogrammable Low Power Platform for Closed-loop Optogenetics

Lookup NU author(s): Dimitrios Firfilionis, Dr Wei Xu, Dr Darren Walsh, Dr Enrique Escobedo-Cousin, Dr Reza Ramezani, Dr Yan Liu, Professor Anthony O'Neill, Ahmad Shah Idil, Professor Andrew Jackson, Professor Patrick Degenaar

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

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Abstract

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from openloop, pacemaker-class, intervention towards fully closedloop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2oC for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip, and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.


Publication metadata

Author(s): Luo J, Firflionis D, Turnbull M, Xu W, Walsh D, Escobedo-Cousin E, Soltan A, Ramezani R, Liu Y, Bailey R, O'Neill A, Shah Idil A, Donaldson N, Constandinou T, Jackson A, Degenaar P

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Biomedical Engineering

Year: 2020

Volume: 67

Issue: 11

Pages: 3004-3015

Print publication date: 01/11/2020

Online publication date: 21/02/2020

Acceptance date: 03/02/2020

Date deposited: 12/02/2020

ISSN (print): 0018-9294

ISSN (electronic): 1558-2531

Publisher: IEEE

URL: https://doi.org/10.1109/TBME.2020.2973934

DOI: 10.1109/TBME.2020.2973934


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