Simulating neuromorphic reservoir computing: Abstract feed-forward hardware models

Recent developments of unconventional hardware using memristors and atomic switch networks has led to renewed interest in hardware neuromorphic solutions. Most hardware models rely upon a reservoir neural network as the basis of any learning, but the distinct differences between software implementations and hardware reality mean what we take for granted in traditional software reservoirs — such as cycles, loops, infinite energy, and discrete time — may be severely limited or unavailable in hardware, raising questions about how a hardware implementation would perform and how to potentially overcome these limitations. Proposed hardware additions, such as an echoer or an input delay mechanism, address some of these limitations.