Objective. Investigating neural population dynamics underlying behavior requires learning
accurate models of the recorded spiking activity, which can be modeled with a Poisson …

Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are
measured with spike trains and larger-scale field potential signals. To study neural …

Objective. Realizing neurotechnologies that enable long-term neural recordings across
multiple spatial-temporal scales during naturalistic behaviors requires new modeling and …

Dynamical encoding models characterize neural activity with low-dimensional hidden states
that dynamically evolve in time and gienerate behavior. Current methods have identified …

Objective. Learning dynamical latent state models for multimodal spiking and field potential
activity can reveal their collective low-dimensional dynamics and enable better decoding of …

Objective. Information encoding in neurons can be described through their response fields.
The spatial response field of a neuron is the region of space in which a sensory stimulus or a …

Neuropsychiatric disorders such as major depression are a leading cause of disability
worldwide with standard treatments, including psychotherapy or medication, failing many …

Inferring complex spatiotemporal dynamics in neural population activity is critical for
investigating neural mechanisms and developing neurotechnology. These activity patterns …

The detection of events in time-series data is a common signal-processing problem. When
the data can be modeled as a known template signal with an unknown delay in Gaussian …

Objective: Studying directed connectivity within spiking neuron networks can help
understand neural mechanisms. Existing methods assume linear time-invariant neural …