Advances in our understanding of brain function, along with the development of neural
interfaces that allow for the monitoring and activation of neurons, have paved the way for …

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving
monkeys. In the 50 years since, great effort has been devoted to understanding how single …

Understanding the dynamical transformation of neural activity to behavior requires new
capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural …

Neural activity exhibits complex dynamics related to various brain functions, internal states
and behaviors. Understanding how neural dynamics explain specific measured behaviors …

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 …

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 …

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or
inputs from other brain regions. To avoid misinterpreting temporally structured inputs as …

Spatiotemporal properties of neuronal population activity in cortical motor areas have been
subjects of experimental and theoretical investigations, generating numerous interpretations …

Point process generalized linear models (PP-GLMs) provide an important statistical
framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic …

Objective. The aim of this work is to improve the state of the art for motor-control with a brain–
machine interface (BMI). BMIs use neurological recording devices and decoding algorithms …