Abstract
We study the stability of two types of mixed states in an oscillator neural network model. Patterns to be stored are represented by complex variables whose amplitudes are either 0 (silent state) or 1 (firing state) and whose phases represent the timing of firing. The mixed states are defined in terms of a certain type of average over some specified number s of memory patterns. We define two types of such mixed states, each of which corresponds to a different type of restriction placed on this set of s memory patterns. The stability of each type is investigated both theoretically and numerically. We find that only one type of a mixed state, which consists of temporally correlated patterns, is stable. Finally, we discuss a possible functional role of such mixed states in information processing.
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.
