An Associative Memory Readout in ESN for Neural Action Potential Detection

This paper describes how echo state networks (ESN) can be used in conjunction with minimum average correlation energy (MACE) filters in order to create a system that can identify spikes in neural recordings. Various experiments using real-world data were used to compare the performance of the ESN-MACE against threshold and matched filter detectors to ascertain the capabilities of such a system in detecting neural action potentials. The experiments demonstrate that the ESN-MACE can correctly detect spikes with lower false alarm rates than established detection techniques since it captures the inherent variability and the covariance information in spike shapes by training.