We present two ways in which measures of information can be used for the design and
analysis of neural networks in both the brain and the computer. In the brain, stimulus is often …

By removing as much as 90% or more of a deep neural network's (DNN's) parameters, a
wide variety of pruning approaches not only allow for DNN compression but also increase …

A brain-computer interface (BCI) is a direct pathway between neural activity and an external
device. Recent clinical BCI systems have demonstrated a promising way to restore …

Perception relies on cortical processes in response to sensory stimuli. Visual input entering
the eyes ascends a cascade of processing steps from the retina to high-level regions of the …

A central question in neuroscience is how expectations shape sensory processing. In the
cerebral cortex, feedforward connections convey sensory information, while feedback …

There is a significant need for more effective treatments for neurological and psychiatric
diseases. Implantable neurostimulators are increasingly used as new therapeutic options for …

Protein engineering has unlocked potential for the biomolecular synthesis of amino acid
sequences with specified functions. With the many implications in enhancing therapy …

Predictive Coding is a hierarchical model of neural computation that approximates
backpropagation using only local computations and local learning rules. An important …

The brain is a complex, interconnected network par excellence. It is made up of intricate
connections of neurons, responsible for transmission of signals throughout the body. Over …

Biological sensory systems appear to rely on canonical nonlinear computations that can be
readily adapted to a broad range of representational objectives. Here we test the hypothesis …