Muscle activity

…the striatum alongside motor output, muscle activity and behavioral video. The project asks how shared and population-specific neural activity encodes movement, using latent-variable models, regression, classification and cross-validation. Work includes reproducible Python analysis and communicating results through figures and methods that may contribute to a poster or publication. No previous neuroscience b

The mapping between neural activity and external behavioral variables can change across experimental conditions and behaviors. For example, in prefrontal cortex, the coding of a stimulus’s direction is different soon after stimulus presentation, versus during the delay period. Similarly, for a monkey performing a wrist movement task, the same linear mappings (decoders) cannot simultaneously predict movements when pushing against a spring, versus freely moving. While neural network-based decoders can have the capacity to accurately perform mappings across changing conditions, they are challengi

…dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the si

…these priors can be set nor how neural activity underlies these fundamental abilities. To answer this, we designed a reaching task in which subjects (human or m

Recent investigations into the coordinated activity of neuronal populations in motor circuits have revealed low-dimensional dynamical features hypothesized to support the generation of patterned motor output. However, such studies have typically relied on vast simplifications - trial-averaging, heavy smoothing - and are often far removed from muscle activity, the true output of the motor system. Thus, it is unclear whether the uncovered dynamical features are primarily useful for intuition-building, or reflect computational mechanisms that operate at the temporal precision of motor control. He

…computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold compu

A central question in motor physiology has been whether motor cortex activity resembles muscle activity, and if not, why not? Over fifty years, extensive observations have failed to provide a concise answer, and the topic remains much debated. To provide a different perspective, we employed a novel behavioral paradigm that affords extensive comparison between time-evolving neural and muscle activity. Single motor-cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ’tr