TY - JOUR T1 - Non-Stationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioural States JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0355-16.2017 SP - ENEURO.0355-16.2017 AU - Alexandre Melanson AU - Jorge F. Mejias AU - James J. Jun AU - Leonard Maler AU - André Longtin Y1 - 2017/03/21 UR - http://www.eneuro.org/content/early/2017/03/21/ENEURO.0355-16.2017.abstract N2 - 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 behavioural 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 non-stationary forcing correctly captures the vastly different timescales of fluctuations observed in the data (∼1 to ∼1000 seconds), and yields long-tailed residence time distributions also consistent with the data. In fact, our model provides a simple mechanism for the emergence of long-tailed distributions in spontaneous animal behaviour. 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 non-stationary 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 data set and then applying principal component analysis over the feature space. Our study is novel in its ability to model non-stationary behavioural data over a wide range of timescales.Significance Statement Animals have the ability to initiate and terminate movement spontaneously. Given an animal moving freely in a constant sensory environment, one might expect to observe trivial behavior. Instead, spontaneous behavior is highly random and consists of a sequence of transitions between behavioural states. Identifying the intrinsic drivers of these transitions is necessary to understand more complex behaviours, and computational models are well suited to investigate the high-level processes governing the transitions. Here we adopt a modeling approach where the neural activity that controls movement is reduced to an effective, low-dimensional process driven by noise and evolving in a nonlinear potential landscape. We show the validity of this approach in the context of spontaneous movement initiation and termination in electric fish. ER -