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eNeuro. 2017 Mar 29;4(2). pii: ENEURO.0355-16.2017. doi: 10.1523/ENEURO.0355-16.2017. eCollection 2017 Mar-Apr.

Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States.

Author information

1
Department of Physics, University of Ottawa , Ottawa, Ontario, Canada , K1N 6N5.
2
Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5; Center for Neural Dynamics, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5; Brain and Mind Research Institute, Department of Medecine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5.
3
Department of Physics, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5; Center for Neural Dynamics, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5; Brain and Mind Research Institute, Department of Medecine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5.

Abstract

The neural basis of spontaneous movement generation is a fascinating open question. Long-term monitoring of fish, swimming freely in a constant sensory environment, has revealed a sequence of behavioral states that alternate randomly and spontaneously between periods of activity and inactivity. We show that key dynamical features of this sequence are captured by a 1-D diffusion process evolving in a nonlinear double well energy landscape, in which a slow variable modulates the relative depth of the wells. This combination of stochasticity, nonlinearity, and nonstationary forcing correctly captures the vastly different timescales of fluctuations observed in the data (∼1 to ∼1000 s), and yields long-tailed residence time distributions (RTDs) also consistent with the data. In fact, our model provides a simple mechanism for the emergence of long-tailed distributions in spontaneous animal behavior. We interpret the stochastic variable of this dynamical model as a decision-like variable that, upon reaching a threshold, triggers the transition between states. Our main finding is thus the identification of a threshold crossing process as the mechanism governing spontaneous movement initiation and termination, and to infer the presence of underlying nonstationary agents. Another important outcome of our work is a dimensionality reduction scheme that allows similar segments of data to be grouped together. This is done by first extracting geometrical features in the dataset and then applying principal component analysis over the feature space. Our study is novel in its ability to model nonstationary behavioral data over a wide range of timescales.

KEYWORDS:

behavioral state transitions; bistability; computer simulations; electric fish; spontaneous movement; stochastic differential equation

PMID:
28374017
PMCID:
PMC5370279
DOI:
10.1523/ENEURO.0355-16.2017
[Indexed for MEDLINE]
Free PMC Article

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