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Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware

Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware
Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware
Building bidirectional biointerfaces is one of the key challenges of modern engineering and medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of biointerface design concern signal stability and power efficiency. The former entails: a) ensuring that biosignal inputs corresponding to the same ground truth (e.g. patient “intentions”) are recorded and interpreted consistently and b) maintaining the mapping from biointerface stimulation outputs to behavioral outputs (e.g. muscle movements). In this work we demonstrate how machine learning techniques, state-of-art nanoelectronics and microfluidics can combine forces to build and test low-power, adaptable biointerfaces that address both key challenges. Specifically, we demonstrate that: 1) we can emulate the input/output transfer characteristics of a structure biological neural network (BNN) with an artificial one (ANN), 2) it is possible to translate the resulting, “ideally trained” ANN into a hardware network using RRAM devices as synapses without significant loss of accuracy, despite concerns in the community about RRAM device reliability and 3) using a very simple mechanism of shifting the active stimulation electrode can fully restore functionality after the initial stimulation site degrades, prolonging the usable lifetime of the biointerface significantly. In this manner we place a key stepping stone towards building self-adjusting, low-power biointerfaces, themselves a foundational stepping stone towards adaptable, low-power bioprostheses.
Accuracy, Artificial neural network, Design, Memristors, Neural interface, Neuro-hybrid systems, Neuromorphic computing, Simulation
0960-0779
Shchanikov, Sergey
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Zuev, Anton
ca22c825-d2c7-4d45-a421-1f7d4d31babc
Bordanov, Ilya
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Danilin, Sergey
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Lukoyanov, Vitaly
fd78de55-dab5-4e08-b103-e53fe2509137
Korolev, Dmitry
91b9352f-e14d-48ff-bcd2-ecdd8e88b832
Belov, Alexey
e2df4a64-e237-4682-874f-387c21d4c706
Pigareva, Yana
eda3b695-3cbe-4189-bac3-ed2d61fcc3a8
Gladkov, Arseny
e219c45a-93f5-45b1-8751-88ceafbaaf7f
Pimashkin, Alexey
236f8378-8bfd-4f39-a519-1e9ecda5c707
Mikhaylov, Alexey
d0bf7c51-fe04-4536-8612-f7ff8edaa17f
Kazantsev, Victor
769d0492-c788-428c-9921-76b3052ef3e1
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Shchanikov, Sergey
04fa3d74-3c00-4ca1-9f25-5e6352c0402d
Zuev, Anton
ca22c825-d2c7-4d45-a421-1f7d4d31babc
Bordanov, Ilya
4702da9a-793a-41a1-b893-a2798952d668
Danilin, Sergey
5e71e859-58c5-4f24-acf7-0b50401bab5b
Lukoyanov, Vitaly
fd78de55-dab5-4e08-b103-e53fe2509137
Korolev, Dmitry
91b9352f-e14d-48ff-bcd2-ecdd8e88b832
Belov, Alexey
e2df4a64-e237-4682-874f-387c21d4c706
Pigareva, Yana
eda3b695-3cbe-4189-bac3-ed2d61fcc3a8
Gladkov, Arseny
e219c45a-93f5-45b1-8751-88ceafbaaf7f
Pimashkin, Alexey
236f8378-8bfd-4f39-a519-1e9ecda5c707
Mikhaylov, Alexey
d0bf7c51-fe04-4536-8612-f7ff8edaa17f
Kazantsev, Victor
769d0492-c788-428c-9921-76b3052ef3e1
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c

Shchanikov, Sergey, Zuev, Anton, Bordanov, Ilya, Danilin, Sergey, Lukoyanov, Vitaly, Korolev, Dmitry, Belov, Alexey, Pigareva, Yana, Gladkov, Arseny, Pimashkin, Alexey, Mikhaylov, Alexey, Kazantsev, Victor and Serb, Alexantrou (2021) Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware. Chaos, Solitons & Fractals, 142, [110504]. (doi:10.1016/j.chaos.2020.110504).

Record type: Article

Abstract

Building bidirectional biointerfaces is one of the key challenges of modern engineering and medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of biointerface design concern signal stability and power efficiency. The former entails: a) ensuring that biosignal inputs corresponding to the same ground truth (e.g. patient “intentions”) are recorded and interpreted consistently and b) maintaining the mapping from biointerface stimulation outputs to behavioral outputs (e.g. muscle movements). In this work we demonstrate how machine learning techniques, state-of-art nanoelectronics and microfluidics can combine forces to build and test low-power, adaptable biointerfaces that address both key challenges. Specifically, we demonstrate that: 1) we can emulate the input/output transfer characteristics of a structure biological neural network (BNN) with an artificial one (ANN), 2) it is possible to translate the resulting, “ideally trained” ANN into a hardware network using RRAM devices as synapses without significant loss of accuracy, despite concerns in the community about RRAM device reliability and 3) using a very simple mechanism of shifting the active stimulation electrode can fully restore functionality after the initial stimulation site degrades, prolonging the usable lifetime of the biointerface significantly. In this manner we place a key stepping stone towards building self-adjusting, low-power biointerfaces, themselves a foundational stepping stone towards adaptable, low-power bioprostheses.

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Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware - Accepted Manuscript
Restricted to Repository staff only until 8 December 2021.
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Accepted/In Press date: 19 November 2020
e-pub ahead of print date: 8 December 2020
Published date: January 2021
Keywords: Accuracy, Artificial neural network, Design, Memristors, Neural interface, Neuro-hybrid systems, Neuromorphic computing, Simulation

Identifiers

Local EPrints ID: 446607
URI: http://eprints.soton.ac.uk/id/eprint/446607
ISSN: 0960-0779
PURE UUID: 6e81aa43-1b3c-480f-9f73-f6e5af27447c

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Date deposited: 16 Feb 2021 17:31
Last modified: 16 Feb 2021 17:35

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Contributors

Author: Sergey Shchanikov
Author: Anton Zuev
Author: Ilya Bordanov
Author: Sergey Danilin
Author: Vitaly Lukoyanov
Author: Dmitry Korolev
Author: Alexey Belov
Author: Yana Pigareva
Author: Arseny Gladkov
Author: Alexey Pimashkin
Author: Alexey Mikhaylov
Author: Victor Kazantsev
Author: Alexantrou Serb

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