Inference of network connectivity from temporally binned spike trains.

Journal: Journal of neuroscience methods
PMID: 38309313

Abstract

BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored.

Authors

  • Adam D Vareberg
    Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States.
  • Ilhan Bok
    Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States.
  • Jenna Eizadi
    Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States.
  • Xiaoxuan Ren
    Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States.
  • Aviad Hai
    Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States.

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