A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on an electronic substrate. This review explores interdisciplinary approaches anchored in machine learning theory that enable the applicability of neuromorphic technologies to real-world, humancentric tasks. We find that (1) recent work in binary deep networks and approximate gradient descentdoi:10.1016/j.isci.2018.06.010 pmid:30240646 pmcid:PMC6123858 fatcat:zo4dvtgo75c7pn6n7tal24fkly