Topic: rotational dynamics

Identifying how brain regions work together to process inputs and generate behavior is a central goal in systems neuroscience. Recently, there has been a surge of interest in characterizing communication between brain regions and shared variability in multi-region neural recordings. In this work, we use a multi-region latent variable model (LVM) to identify computations shared-across and independent-to specific brain regions in spiking neural population data from two different animals. Here, we use a multi-region latent variable model (LVM) to identify computations shared-across and independen

One of the central questions in motor neuroscience is how the population of motor cortical neurons control voluntary movement. Much of our current understanding of this question is based on so-called center-out reaching tasks that involve reaching from a single start location to various spatial targets --- a situation that confounds reach direction with limb posture at the spatial target. Because of this confound, previous studies lack the required condition variability to probe the geometry of neural dynamics (NDs) for movements across different postures and extents. To address this confound,

Spatiotemporal properties of motor cortex neuronal activity have been extensively studied, with the interpretations of voluntary movement mechanisms being the key issue. Two competing models, representational and dynamical, explain the conversion of neuronal activity into limb movements. The dynamical model uses the joint PCA method, which holistically characterizes neuronal oscillations by maximizing data rotational features. Different interpretations tried to explain the presence of rotational dynamics in neuronal patterns (Fig. 1a, c). Yet, their exact nature remains poorly understood.Here