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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control.

Amy L Orsborn¹, Helene G Moorman², Simon A Overduin³

¹UC Berkeley-UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA.

|June 20, 2014

Summary

Neuroplasticity aids neuroprosthetic control, but system changes pose challenges. Adaptive decoding methods can enhance this learning, improving performance and skill retention for real-world applications.

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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Published on: March 10, 2011

Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Engineering

Background:

Neuroplasticity is crucial for developing effective neuroprostheses.
System nonstationarities, like changing neural activity, can hinder neuroplasticity and prosthetic control.
Adaptive decoding methods offer a potential solution for nonstationary neuroprosthetic systems.

Purpose of the Study:

To investigate how decoder adaptation can shape neuroplasticity in neuroprosthetic control.
To examine the utility of neuroplasticity in scenarios relevant to real-world neuroprostheses, including nonstationary neural recordings and altered control contexts.
To determine if beneficial neuroplasticity can coexist with decoder adaptation.

Main Methods:

Nonhuman primates were trained to control a cursor for a reaching task using neural activity.
Two control contexts were used: with and without simultaneous arm movements.
Decoder adaptation was implemented to enhance initial performance and correct for neural recording drift.

Main Results:

Decoder adaptation improved initial neuroprosthetic control performance.
The adaptive decoding approach compensated for changes in neural recordings.
Beneficial neuroplasticity was observed, leading to improved performance, skill retention, and reduced interference from endogenous motor networks.
Neuroprosthetic control was enhanced even with simultaneous arm movements.

Conclusions:

Decoder adaptation can effectively shape neuroplasticity for improved neuroprosthetic control.
Neuroplasticity is beneficial for real-world neuroprostheses, even in the presence of adaptive decoding.
These findings support the use of adaptive decoding to leverage neuroplasticity for robust neuroprosthetic function.