Spiking neural networks for higher-level information fusion
2004
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.
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