Topic: linear dynamical system

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behavior. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence primary visual cortex (V1) and higher visual area LM had on each other was surprisingly variable over time. Both feedforward and feedback pathways

Neuroscientific data exhibits complex, nonlinear dynamics, varying in time and over trials. A growing body of work has focused on capturing nonlinear dynamics, doing so in increasingly complex ways for modeling and inference. Meanwhile, capturing the dependency on experimental conditions surrounding the neural recordings remains understudied, and few methods leverage the correlation structure across experimental conditions. This poses difficulties for model estimation as well as any analyses comparing and interpolating between conditions. In this work, we develop the Gaussian Process Linear Dy

Recent findings have shown that the brain’s internal representation of the external world shifts over days, a phenomenon termed representational drift [Driscoll et al., 2017, Rule et al., 2020]. Thus, it becomes crucial to account for such non-stationarities when investigating the roles of circuits in the brain. While linear dynamical system (LDS) models are commonly used to characterize the dynamics of neural population activity, they assume a stable mapping from the latent states to neural activity. This limits their ability to capture non-stationary processes---such as representational drif