PT - JOURNAL ARTICLE AU - Tie XU AU - Omri Barak TI - Dynamical timescale explains marginal stability in excitability dynamics AID - 10.1523/JNEUROSCI.2340-16.2017 DP - 2017 Mar 27 TA - The Journal of Neuroscience PG - 2340-16 4099 - http://www.jneurosci.org/content/early/2017/03/27/JNEUROSCI.2340-16.2017.short 4100 - http://www.jneurosci.org/content/early/2017/03/27/JNEUROSCI.2340-16.2017.full AB - Action potentials, taking place over milliseconds, are the basis of neural computation. Yet, 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 by 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.SIGNIFICANCE STATEMENTExcitability is the basis for all neural computations, and its long term dynamics reveal a complex combination of many timescales. We discover that neural excitability operates under a marginally stable regime where the system is dominated by internal fluctuation while retaining high sensitivity to externally varying stimuli. We offer a novel approach to model excitability dynamics by assuming that the recovery timescale is itself a dynamic variable. Our model is able to capture a wide range of experimental phenomena using few parameters with significantly higher predictive power than previous models.