Progress in Neuroengineering for brain repair: New challenges and open issues
By Gabriella Panuccio, M. Semprini, Lorenzo Natale, S. Buccelli, I. Colombi, M. Chiappalone. 2018
33 citations recorded by the source corpus.
Background: In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome. Results: In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise. Conclusions: Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neurology
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In Cognitive Neuroscience
Citations in the corpus
- Reinforcement Learning in Healthcare: A Survey (2019, methodology): “itly maximize the effectiveness of stimulation, while simultaneously minimizing the overall amount of stimulation applied thus reducing cell damage and preserving cognitive and neurological functions [157]. Guez et al. [158], [159], [160] applied the BRL method, FQI-ERT, to optimize a deepbrain stim”