Dynamical timescale explains marginal stability in excitability dynamics

Tie Xu, Omri Barak

Research output: Contribution to journalArticlepeer-review

Abstract

Action potentials, taking place over milliseconds, are the basis of neural computation. However, the dynamics of excitability over longer, behaviorally relevant timescales remain underexplored. A recent experiment used long-term recordings from single neurons to reveal multiple timescale fluctuations in response to constant stimuli, along with more reliable responses to variable stimuli. Here, we demonstrate that this apparent paradox is resolved if neurons operate in a marginally stable dynamic regime, which we reveal using a novel inference method. Excitability in this regime is characterized by large fluctuations while retaining high sensitivity to external varying stimuli. A new model with a dynamic recovery timescale that interacts with excitability captures this dynamic regime and predicts the neurons’ response with high accuracy. The model explains most experimental observations under several stimulus statistics. The compact structure of our model permits further exploration on the network level.

Original languageEnglish
Pages (from-to)4508-4524
Number of pages17
JournalJournal of Neuroscience
Volume37
Issue number17
DOIs
StatePublished - 26 Apr 2017

Keywords

  • Adaptation
  • Excitability
  • Inference
  • Model
  • Multiple timescale
  • Nonlinear dynamics

All Science Journal Classification (ASJC) codes

  • General Medicine

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