Next-generation interfaces for studying neural function

被引:0
|
作者
James A. Frank
Marc-Joseph Antonini
Polina Anikeeva
机构
[1] Massachusetts Institute of Technology,Research Laboratory of Electronics
[2] Massachusetts Institute of Technology,McGovern Institute for Brain Research
[3] Harvard/MIT Health Science & Technology Graduate Program,Department of Material Science and Engineering
[4] Massachusetts Institute of Technology,undefined
来源
Nature Biotechnology | 2019年 / 37卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Monitoring and modulating the diversity of signals used by neurons and glia in a closed-loop fashion is necessary to establish causative links between biochemical processes within the nervous system and observed behaviors. As developments in neural-interface hardware strive to keep pace with rapid progress in genetically encoded and synthetic reporters and modulators of neural activity, the integration of multiple functional features becomes a key requirement and a pressing challenge in the field of neural engineering. Electrical, optical and chemical approaches have been used to manipulate and record neuronal activity in vivo, with a recent focus on technologies that both integrate multiple modes of interaction with neurons into a single device and enable bidirectional communication with neural circuits with enhanced spatiotemporal precision. These technologies not only are facilitating a greater understanding of the brain, spinal cord and peripheral circuits in the context of health and disease, but also are informing the development of future closed-loop therapies for neurological, neuro-immune and neuroendocrine conditions.
引用
收藏
页码:1013 / 1023
页数:10
相关论文
共 50 条
  • [21] Next-Generation Sequencing: Next-Generation Quality in Pediatrics
    Wortmann, Saskia B.
    Spenger, Johannes
    Preisel, Martin
    Koch, Johannes
    Rauscher, Christian
    Bader, Ingrid
    Mayr, Johannes A.
    Sperl, Wolfgang
    PADIATRIE UND PADOLOGIE, 2018, 53 (06): : 278 - 283
  • [22] Next-Generation Sequencing Demands Next-Generation Phenotyping
    Hennekam, Raoul C. M.
    Biesecker, Leslie G.
    HUMAN MUTATION, 2012, 33 (05) : 884 - 886
  • [23] Next-generation sequencing for next-generation breeding, and more
    Tsai, Chung-Jui
    NEW PHYTOLOGIST, 2013, 198 (03) : 635 - 637
  • [24] Reliable Next-Generation Cortical Interfaces for Chronic Brain-Machine Interfaces and Neuroscience
    Maharbiz, Michel M.
    Muller, Rikky
    Alon, Elad
    Rabaey, Jan M.
    Carmena, Jose M.
    PROCEEDINGS OF THE IEEE, 2017, 105 (01) : 73 - 82
  • [25] Next-generation sequencing of the next generation
    Darren J. Burgess
    Nature Reviews Genetics, 2011, 12 : 78 - 79
  • [26] Neural Networks and Graph Algorithms with Next-Generation Processors
    Hamilton, Kathleen E.
    Schuman, Catherine D.
    Young, Steven R.
    Imam, Neena
    Humble, Travis S.
    2018 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW 2018), 2018, : 1194 - 1203
  • [27] Next-generation probes, particles, and proteins for neural interfacing
    Rivnay, Jonathan
    Wang, Huiliang
    Fenno, Lief
    Deisseroth, Karl
    Malliaras, George G.
    SCIENCE ADVANCES, 2017, 3 (06):
  • [28] Next-generation flexible neural and cardiac electrode arrays
    Kim J.
    Lee M.
    Rhim J.S.
    Wang P.
    Lu N.
    Kim D.-H.
    Biomedical Engineering Letters, 2014, 4 (02) : 95 - 108
  • [29] THE NEXT-GENERATION
    GREENGARD, S
    PERSONNEL JOURNAL, 1994, 73 (03) : 40 - &
  • [30] NGSNGS: next-generation simulator for next-generation sequencing data
    Henriksen, Rasmus Amund
    Zhao, Lei
    Korneliussen, Thorfinn Sand
    BIOINFORMATICS, 2023, 39 (01)