Hierarchical pulse-coupled neural network model with temporal coding and emergent feature binding mechanism

We propose a convolutional-type, spiking neural network model with explicit timing structure of pulse trains (pulse packet) used for encoding/decoding local visual features. The pulse phase modulating (PPM) synapses function as feature encoders that reflect an internal representation of higher class feature in terms of spike timing. PPM synapses together with a local bus that transmits the structured pulse packet signals form convergent connections to a feature detecting neuron. Distributed, local timing neurons are introduced for an event-driven, stable, and accurate control of the pulse packet signals propagated in the hierarchical, synchronously spiking network