Abstract
An artificial network is described that can learn, recognize, and generate higher-order temporal-spatial sequences. It consists of three parts: (1) comparator units, (2) a parallel array of artificial neural networks that are derived from the visual-vestibular networks of the snail Hermissenda, as well as hippocampal neuroanatomy, and (3) delayed feedback lines from the output of the system to the neural network layer. Its advantages include short training time, fast and accurate retrievals, toleration of spatial noise and temporal gaps in test sequences, and ability to store a large number of temporal sequences consisting of non-orthogonal spatial patterns.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
