Title :
Spatiotemporal Pattern Recognition via Liquid State Machines
Author :
Goodman, Eric ; Ventura, Dan
Author_Institution :
Sandia Nat. Lab., Albuquerque
Abstract :
The applicability of complex networks of spiking neurons as a general purpose machine learning technique remains open. Building on previous work using macroscopic exploration of the parameter space of an (artificial) neural microcircuit, we investigate the possibility of using a liquid state machine to solve two real-world problems: stockpile surveillance signal alignment and spoken phoneme recognition.
Keywords :
neural nets; pattern recognition; artificial neural microcircuit; complex networks; liquid state machines; machine learning; spatiotemporal pattern recognition; spiking neurons; spoken phoneme recognition; stockpile surveillance signal alignment; Complex networks; Encoding; Laboratories; Machine learning; Neural microtechnology; Neurons; Pattern recognition; Spatiotemporal phenomena; Surveillance; Tellurium;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.246880
