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• W3183060718 abstract "Implementation of effective brain or neural stimulation protocols for restoration of complex sensory perception, e.g., in the visual domain, is an unresolved challenge. By leveraging the capacity of deep learning to model the brain’s visual system, optic nerve stimulation patterns could be derived that are predictive of neural responses of higher-level cortical visual areas in silico. This novel approach could be generalized to optimize different types of neuroprosthetics or bidirectional brain-computer interfaces (BCIs). Implementation of effective brain or neural stimulation protocols for restoration of complex sensory perception, e.g., in the visual domain, is an unresolved challenge. By leveraging the capacity of deep learning to model the brain’s visual system, optic nerve stimulation patterns could be derived that are predictive of neural responses of higher-level cortical visual areas in silico. This novel approach could be generalized to optimize different types of neuroprosthetics or bidirectional brain-computer interfaces (BCIs). While neuroprosthetics for restoration of movement have substantially advanced over the last years, implementing effective sensory neuroprosthetics proved very challenging because effective brain/neural stimulation protocols were lacking. In this issue of Patterns, Romeni et al. propose a method to optimize optic nerve stimulation parameters for vision restoration using an artificial brain network.1Romeni S. Zoccolan D. Micera S. A machine learning framework to optimize optic nerve electrical stimulation for vision restoration.Patterns. 2021; 2https://doi.org/10.1016/j.patter.2021.100286Abstract Full Text Full Text PDF Scopus (3) Google Scholar By performing in silico experiments, they found that their stimulation framework achieves results comparable to natural vision. Such work highlights the potential of neurotechnology informed by artificial models of the brain and suggests that artificial neural networks may substantially aid the development of bidirectional brain-computer interfaces (BCIs) restoring both perception and action. Neuroprosthetics, i.e., systems that substitute for motor, sensory, or cognitive functions, require neural interfaces that can interact with the brain. Such interaction builds on brain/neural signal decoding, e.g., to restore motor function in paralysis,2Soekadar S.R. Witkowski M. Gomez C. Opisso E. Medina J. Cortese M. Cempini M. Carrozza M.C. Cohen L.G. Birbaumer N. Vitiello N. Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia.Science Robotics. 2016; 1: eaag3296Crossref PubMed Scopus (110) Google Scholar and stimulation of neural tissue or nerves, e.g., for restoration of sensory function. Based on operant conditioning of neural cell assemblies and machine learning, motor and cognitive neuroprosthetics have achieved remarkable versatility, e.g., continuous control of individual finger, wrist, and hand movements using surface or implanted functional electric stimulation (FES).3Bouton C.E. Shaikhouni A. Annetta N.V. Bockbrader M.A. Friedenberg D.A. Nielson D.M. Sharma G. Sederberg P.B. Glenn B.C. Mysiw W.J. et al.Restoring cortical control of functional movement in a human with quadriplegia.Nature. 2016; 533: 247-250Crossref PubMed Scopus (449) Google Scholar,4Ajiboye A.B. Willett F.R. Young D.R. Memberg W.D. Murphy B.A. Miller J.P. Walter B.L. Sweet J.A. Hoyen H.A. Keith M.W. et al.Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration.Lancet. 2017; 389: 1821-1830Abstract Full Text Full Text PDF PubMed Scopus (365) Google Scholar Recently, such a system was successfully enhanced by somatosensory cortex stimulation that substantially improved prosthetic hand and arm control by evoking tactile sensations.5Flesher S.N. Downey J.E. Weiss J.M. Hughes C.L. Herrera A.J. Tyler-Kabara E.C. Boninger M.L. Collinger J.L. Gaunt R.A. A brain-computer interface that evokes tactile sensations improves robotic arm control.Science. 2021; 372: 831-836Crossref PubMed Scopus (58) Google Scholar In another study, an implantable cortical interface was employed for high-performance brain-to-text communication via imagined handwriting long after motor function was lost.6Willett F.R. Avansino D.T. Hochberg L.R. Henderson J.M. Shenoy K.V. High-performance brain-to-text communication via handwriting.Nature. 2021; 593: 249-254Crossref PubMed Scopus (86) Google Scholar Besides restoration of motor and cognitive functions, neuroprosthetics have also successfully restored sensory function, e.g., in the auditory domain using cochlear implants.7Macherey O. Carlyon R.P. Cochlear implants.Curr. Biol. 2014; 24: R878-R884Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar Similarly, simple vision could be restored using retinal implants,8Zrenner E. Will retinal implants restore vision?.Science. 2002; 295: 1022-1025Crossref PubMed Scopus (627) Google Scholar but restoration of more detailed visual perception was challenging due to the intricate spatiotemporal patterns of retinal or optic nerve activity that encode such perception. Building on the postulate that “every good regulator of a system must be a model of that system”,9Conant R.C. Ross Ashby W. Every good regulator of a system must be a model of that system.Int. J. Syst. Sci. 1970; 1: 89-97Crossref Scopus (635) Google Scholar Romeni et al. (2021)1Romeni S. Zoccolan D. Micera S. A machine learning framework to optimize optic nerve electrical stimulation for vision restoration.Patterns. 2021; 2https://doi.org/10.1016/j.patter.2021.100286Abstract Full Text Full Text PDF Scopus (3) Google Scholar created an artificial model of the visual system by capitalizing on a remarkable property of convolutional neural networks (CNNs). When such artificial brain networks are trained to classify images, the resulting model turns out to be highly predictive of neural responses of mid-level visual areas (e.g., V4) of the brain’s ventral stream.10Yamins D.L. Hong H. Cadieu C.F. Solomon E.A. Seibert D. DiCarlo J.J. Performance-optimized hierarchical models predict neural responses in higher visual cortex.Proc. Natl. Acad. Sci. USA. 2014; 111: 8619-8624Crossref PubMed Scopus (716) Google Scholar Importantly, this type of model can be pre-trained. Then, using the resulting rich artificial neuronal model of the visual system, inputs to the network could be optimized, representing electrical stimulation of the optic nerve that best activated an abstract cortical layer coding for the object whose sensory input was to be enhanced. Psychophysical data exhibiting large inter-subject and within-subject variabilities were obtained with healthy human volunteers. Here, the authors showed that the performance of their proposed framework was in general comparable to the healthy volunteers’ performance and even exceeded it in easy visual classification tasks. While the approach is promising, a few key issues remain to be resolved. The presented technique must be subject to in vivo validation. Furthermore, optimization of stimulation parameters is currently performed on an image-by-image basis and thus cannot be performed in real time. In the future, a continuous mapping from video recordings to optic nerve stimulation parameters will be necessary for restoration of dynamic vision. Regardless of these obstacles, it is conceivable that neuroprosthetics of the future will broadly use artificial brain networks to increase the scope of interaction with the human brain. In this context, however, implementation of bidirectional BCIs or neuroprosthetics that merge an artificial and biological cognitive system in a hybrid mind raises a number of neuroethical questions—some of which are still not fully charted.11Soekadar S.R. Chandler J. Ienca M. Bublitz C. On the verge of the hybrid mind.Morals & Machines. 2021; 1: 30-43Crossref Google Scholar This research is supported in part by the European Research Council (ERC) under the project NGBMI ( 759370 ), ERA-NET Neuron under the project HYBRIDMIND ( 01GP2121B ), and the Einstein Stiftung Berlin . A machine learning framework to optimize optic nerve electrical stimulation for vision restorationRomeni et al.PatternsJune 16, 2021In BriefWe formulated a computational framework for the optimization of optic nerve stimulation patterns. We have implemented a model of the primate visual system, and an algorithm that allows the evolution of an optic nerve stimulation protocol that induces activation corresponding to natural visual stimuli in a given brain region and, consequently, a specified visual sensation. This could pave the way for novel machine-learning-based optimization of optic nerve stimulation to produce naturalistic sensations in blind patients. Full-Text PDF Open Access" @default.
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• W3183060718 title "Advancing sensory neuroprosthetics using artificial brain networks" @default.
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