In 2030, a robotic emissary launched from Earth seven years earlier will lay eyes on a metal world never seen from close range. That NASA spacecraft, known as Psyche, will carry with it a number of instruments designed to spy on the the metallic world called 16 Psyche as it circles the sun. The scientific study of 16 Psyche may indirectly help make asteroid mining -- a science fiction-sounding concept that literally involves extracting rare minerals, water or other materials from a rock floating in space -- a reality. "I think the most important things we're going to discover are: What are the surface conditions of a metal asteroid like?" Psyche mission principal investigator Lindy Elkins-Tanton said during a press conference. "What might landing challenges be? Is it covered with some kind of regolith [dirt]? Is it actually a smooth and hard surface?"

The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002) demonstrated that many of the filtering properties of the cochlea could be explained in terms of efficient coding of natural sounds. This model, however, did not account for properties such as phase-locking or how sound could be encoded in terms of action potentials. Here, we extend this theoretical approach with algorithm for learning efficient auditory codes using a spiking population code. Here, we propose an algorithm for learning efficient auditory codes using a theoretical model for coding sound in terms of spikes.

The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002) demonstrated that many of the filtering properties of the cochlea could be explained in terms of efficient coding of natural sounds. This model, however, did not account for properties such as phase-locking or how sound could be encoded in terms of action potentials. Here, we extend this theoretical approach with algorithm for learning efficient auditory codes using a spiking population code. Here, we propose an algorithm for learning efficient auditory codes using a theoretical model for coding sound in terms of spikes.

The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002) demonstrated that many of the filtering properties of the cochlea could be explained in terms of efficient coding of natural sounds. This model, however, did not account for properties such as phase-locking or how sound could be encoded in terms of action potentials. Here, we extend this theoretical approach with algorithm for learning efficient auditory codes using a spiking population code. Here, we propose an algorithm for learning efficient auditory codes using a theoretical model for coding sound in terms of spikes.