Pattern retrieval in a three-layer oscillatory network with a context dependent synaptic connectivity

We propose a network solution for memory pattern retrieval in an oscillatory network based on a context dependent Hebbian connectivity. The model is composed of three interacting layers of spiking neurons with excitatory and inhibitory synaptic connections. Information patterns are stored in the memory using a symmetric Hebbian matrix and can be retrieved in response to a definite stimulus pattern. The patterns are encoded as distributions of phases of the oscillatory network units. We include in the network architecture an intermediate layer of excitable (non-oscillatory) interneurons. This layer provides a kind of pre-processing by filtering the in-phase or the anti-phase components of the input pattern. Then, only a part of Hebbian connections defined by the input (a “context dependent connectivity”) is further used for the memory retrieval. Being supplied with an oscillatory clock signal the interneurons drive the signal propagation pathways in the feedforward architecture and, hence, reduce the number of effective connections needed for the retrieval. The oscillation phase stability problem for the in-phase and anti-phase locking modes is investigated. Information characteristics and efficiency of the context dependent retrieval are discussed and compared with traditional oscillatory associative memory models.