Effect of complexity in an oscillatory neural network - Relation to human-like reasoning
Several experimental findings suggest that synchronization or coherence of neuronal firings may play an important role in information processing in the brain. This led to the study of phase and frequency segmentation in ''temporal-coding'' systems including coupled oscillator systems or oscillatory neural networks. Functionally, such systems differ from conventional (non-temporal) neural networks in that, in addition to spatial activation pattern as in conventional neural networks, they provide various temporal patterns which can be considered as additional means to code information. For example, they are expected to have the capacity of performing symbolic inferences. In this paper, spatiotemporal aspect of a class of oscillatory neural networks is examined in relation to graph-theoretic characteristics. It was observed that as the number of oscillators increases, the system provides various types of solutions resembling human response to complex input. For example, the model qualitatively reproduces human response time characteristic in a class of visual search experiment. Application of the phenomena to human-like reasoning is examined.
complexity oscillation synchronization psychology graph theory conjunction search attention clustering approximate reasoning