|Title||Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Melanson, Alexandre, Jorge F. Mejias, James J. Jun, Leonard Maler, and Andre Longtin|
|Date Published||2017 Mar-Apr|
|Keywords||Animals, Behavior, Computer Simulation, Electric Fish, Electric Organ, Locomotion, Models, Biological, Monte Carlo Method, Nonlinear Dynamics, Stochastic Processes, Time Factors|
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.
|PubMed Central ID||PMC5370279|