Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.

Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold.

Dynamical flexible inference of nonlinear latent structures in neural population activity.

Inferring complex spatiotemporal dynamics in neural population activity is critical for investigating neural mechanisms and developing neurotechnology. These activity patterns are noisy observations of lower-dimensional latent factors and their nonlinear dynamical structure. A major unaddressed challenge is to model this nonlinear structure, but in a manner that allows for flexible inference, whether causally, non-causally, or in the presence of missing neural observations.

Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.

Motor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored.

Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification.

Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics.

A Multiscale Dynamical Modeling and Identification Framework for Spike-Field Activity.

Dynamical encoding models characterize neural activity with low-dimensional hidden states that dynamically evolve in time and gienerate behavior. Current methods have identified these models from single-scale activity, either spikes or fields. However, behavior is simultaneously encoded across multiple spatiotemporal scales of activity, from spikes of individual neurons to neural population activity measured through fields.

Identifying multiscale hidden states to decode behavior.

A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior.

An unsupervised learning algorithm for multiscale neural activity.

Technological advances have enabled the simultaneous recording of multiscale neural activity consisting of spikes, local field potential (LFP), and electrocorticogram (ECoG). Developing models that describe the encoding of behavior within multiscale activity is essential both for understanding neural mechanisms and for various neurotechnologies such as brain-machine interfaces (BMI). Multiscale recordings consist of signals with different statistical profiles and time-scales.