Hierarchical learning of conjunctive concepts in spiking neural networks

The temporal correlation hypothesis proposes that synchronous activity in different regions of the brain describes integral entities. This temporal binding approach is a possible solution to the longstanding binding problem of representing composite objects. To complement the dynamic nature of temporal binding, a recruitment learning method has been proposed for providing long-term storage. We improve the recruitment method to use a more realistic and powerful spiking neuron model. However, using continuous-time spiking neurons and brain-like connectivity assumptions poses new problems in hierarchical recruitment. First, we propose timing parameter constraints for recruitment over asymmetrically delayed lines. Second, we calculate required feedforward excitatory and lateral inhibitory connection densities for stable propagation of activity independent of network size.