Spiking neural networks for higher-level information fusion

This paper presents a novel approach to higher-level (2+) information fusion and knowledge representation using semantic networks composed of coupled spiking neuron nodes. Networks of spiking neurons have been shown to exhibit synchronization, in which sub-assemblies of nodes become phase locked to one another. This phase locking reflects the tendency of biological neural systems to produce synchronized neural assemblies, which have been hypothesized to be involved in feature binding. The approach in this paper embeds spiking neurons in a semantic network, in which a synchronized sub-assembly of nodes represents a hypothesis about a situation. Likewise, multiple synchronized assemblies that are out-of-phase with one another represent multiple hypotheses. The initial network is hand-coded, but additional semantic relationships can be established by associative learning mechanisms. This approach is demonstrated with a simulated scenario involving the tracking of suspected criminal vehicles between meeting places in an urban environment.