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Advancing sensory neuroprosthetics using artificial brain networks.

David Haslacher1, Khaled Nasr1, Surjo R Soekadar1

  • 1Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Psychotherapy (CCM), Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.

Patterns (New York, N.Y.)
|July 21, 2021
PubMed
Summary
This summary is machine-generated.

This study uses deep learning to create brain stimulation patterns for restoring vision. These optimized patterns predict neural responses, advancing neuroprosthetics and brain-computer interfaces.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Restoring complex sensory perception, particularly vision, via brain stimulation remains a significant challenge.
  • Current neuroprosthetic and brain-computer interface (BCI) technologies require optimized stimulation protocols.

Discussion:

  • Deep learning models can simulate the brain's visual system to predict neural responses.
  • This in silico approach allows for the derivation of optic nerve stimulation patterns.
  • The generated patterns are designed to elicit specific responses in higher-level cortical visual areas.

Key Insights:

  • Novel optic nerve stimulation patterns were developed using deep learning.
  • These patterns accurately predict neural responses in silico.
  • The methodology offers a pathway to enhance visual perception restoration.

Outlook:

  • The approach can be generalized to optimize various neuroprosthetics.
  • This research paves the way for improved bidirectional brain-computer interfaces.
  • Further development could lead to more effective restoration of sensory functions.