We present a SNN model that uses spike-latency coding and winner-take-all inhibition to efficiently represent visual objects with as little as 15 spikes per neuron.
Who We Are
We are an interdisciplinary group interested in the computational modeling of human, animal, computer, and prosthetic vision to elucidate the science behind bionic technologies that may one day restore useful vision to people living with incurable blindness.
Our group combines expertise in computer science/engineering, neuroscience, and psychology. All our team members are computationally minded and have a keen interest in vision and medical applications. Our research projects range from predicting neurophysiological data with deep learning to building biophysical models of electrical brain stimulation, and from studying perception in people with visual impairment to developing prototypes of novel visual accessibility aids using virtual and augmented reality.
Importantly, our ongoing collaborations with several visual prosthesis manufacturers put our laboratory in a unique position to empirically validate our theoretical findings across multiple bionic eye technologies.
