Two-level hierarchy with sparsely and temporally coded patterns and its possible functional role in information processing.
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.
Nomura
Masaki
M
Aoyagi
Toshio
T
Okada
Masato
M
D008606:Mental Processes / Q000502:physiology; D016571:Neural Networks, Computer