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Dynamic representations in networked neural systems

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NATURE NEUROSCIENCE
卷 23, 期 8, 页码 908-917

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NATURE PORTFOLIO
DOI: 10.1038/s41593-020-0653-3

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资金

  1. John D. and Catherine T. MacArthur Foundation
  2. Alfred P. Sloan Foundation
  3. ISI Foundation
  4. Paul Allen Foundation
  5. Army Research Laboratory [W911NF-10-2-0022]
  6. Army Research Office [Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474, DCIST-W911NF-17-2-0181]
  7. Office of Naval Research
  8. National Institute of Mental Health [2-R01-DC-00920911, R01-MH112847, R01-MH107235, R21-M MH-106799]
  9. National Institute of Child Health and Human Development [1R01-HD086888-01]
  10. National Institute of Neurological Disorders and Stroke [R01-NS099348]
  11. National Science Foundation [BCS-1441502, BCS-1430087, NSF PHY-1554488, BCS-1631550]

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A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research. Recent studies separately address the neural representation of stimuli and its dynamics in networks that model neural interactions. Ju and Bassett review such recent advances and discuss the integration of neural representations and network models.

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