Digital neuromorphic design of a Liquid State Machine for real-time processing

Abstract

The Liquid State Machine (LSM) is a form of reservoir computing which emulates the brains capability of processing spatio-temporal data. This type of network generates highly descriptive responses to continuous input streams. The response is then used to extract information about the input stream. A single LSM network can be used as a generic intelligent processor that processes different streams of data (or) on same stream of data to extract different features. The LSM has been shown to perform well in tasks dependent on a systems behavior through time. The LSM's intrinsic memory and its reduced training complexity make it a suitable choice for hardware implementations for spatio-temporal applications. Existing behavioral models of LSM cannot process real time data due to their hardware complexity or inability to deal with real-time data or both. The proposed model focuses on a simple liquid design that exploits spatial locality and is capable of processing real time data. The model is evaluated for EEG seizure detection with an accuracy of 84.2% and for user identification based on walking pattern with an accuracy of 98.4%.