Deep neural networks are commonly developed and trained in 32-bit floating point format.
Significant gains in performance and energy efficiency could be realized by training and …

Neural activity in motor cortex during reach and grasp movements shows modulations in a
broad range of signals from single-neuron spiking activity (SA) to various frequency bands in …

Deep learning has enjoyed a great deal of success because of its ability to learn useful
features for tasks such as classification. But there has been less exploration in learning the …

The Deep Learning (DL) community sees many novel topologies published each year.
Achieving high performance on each new topology remains challenging, as each requires some …

A prominent feature of motor cortex field potentials during movement is a distinctive low-frequency
local field potential (lf-LFP) (<4 Hz), referred to as the movement event-related …

Computational assignment of function to proteins with no known homologs is still an unsolved
problem. We have created a novel, function-based approach to protein annotation and …

Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding
improved sample efficiency, the ability to deal with long time horizons and transferability of …

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in
an image, the brain compares input signals and goals to elicit the correct behavior. We …

The cerebral cortex needs to maintain information for long time periods while at the same
time being capable of learning and adapting to changes. The degree of stability of …

Rapid and flexible learning during behavioral choices is critical to our daily endeavors and
constitutes a hallmark of dynamic reasoning. An important paradigm to examine flexible …