Topic: recurrent switching linear dynamical systems

Recent work has indicated that functional connections between C. elegans neurons can be clustered by their role in behavior, i.e. forward vs. reverse crawling vs. turning (Linderman et al. 2019). Furthermore they show how data-driven dynamical systems, e.g., the recurrent switching linear dynamical systems (rSLDS), can be learned in an unsupervised way from whole-brain recordings (Kato et al. 2015). However, these approaches ascribe a discrete overall state to the system at each time point, potentially missing different dimensions of variability in the data. For example, such models cannot fle

Unsupervised segmentation of behavior and neural activity into simpler alternating states is providing unprecedented insights onto the neural underpinnings of decision-making. Recurrent Switching Linear Dynamical Systems (rSLDS) models use observations or continuous latent activity to guide the switching in states, providing significant advantages in capturing important temporally and spatially sequential dependencies. In trying to allow for unbounded complexity in the discrete states, most approaches have focused on Dirichlet Process (DP) mixture models. Such non-parametric Bayesian models re

A prevailing theory in goal-directed decision-making is that the brain accumulates evidence over time to make perceptual decisions. However, even after two decades of studies on evidence accumulation, it is still unclear how neuronal populations cooperate to dynamically represent decision variables. This is because of a lack of tools for gaining experimental control over variables of interest to test their relationship to behavior. To address this gap, we develop a state-space control framework to causally interact with decision-related latent variables. To expose decision variables to target

Locomotor behaviors are composed of ordered transitions between motor states required to produce a functional outcome. However, the circuit mechanisms by which the brain generates a sequential ordering of motor actions for constructive behavioral output, particularly in freely-behaving animals, is largely unknown. With 302 neurons within a well-defined connectome and a library of genetic tools, complex yet discernible behaviors, and recent advances in brain-wide imaging, the nematode Caenorhabditis elegans offers a unique opportunity to study how the brain generates behavior. To better underst