A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods for exploring the link between neural activity and behavior rely on the analysis of static images and brain scans, as opposed to the dynamic evolution of neuronal activity over time. Dynamical models, mathematical or computational approaches for describing the evolution of a system over time provide a valuable alternative to these methods.
Most dynamical models introduced in the past were linear, which means that they assumed that changes in neural activity would follow a simple structure. While linear models tend to be easier to implement and interpret, they often fail to accurately capture complex neural dynamics. This has motivated some neuroscientists and computer scientists to develop other dynamical models that can describe different types of linearity and non-linear dynamics.
Researchers at University of Southern California and University of Pennsylvania recently introduced a new nonlinear dynamical modeling framework based on recurrent neural networks (RNNs) that addresses some of the limitations of dynamical models for neuroscience research introduced in the past. This new framework, outlined in a paper published in Nature Neuroscience , can be used to model both behaviorally relevant and other neural dynamics, yet it dissociates the two and prioritizes dynamics that are .