Oscillatory Dynamic Link Matching for Pattern Recognition

The ”dynamic link matching” (DLM) has been first proposed by Konen et al. [1] to solve the visual correspondence problem. The approach consists of two layers of neurons connected to each other through synaptic connections constrained to some normalization. The reference pattern is applied to one of the layers and the pattern to be recognized to the other. The dynamics of the neurons are chosen in such a way that ”blobs” are formed randomly in the layers. If the features of two blobs, each belonging to a different layer, are sufficiently similar, then weight strengthening between the blobs and activity similarity will be observed between sets of similar blobs. The size of the blobs remains fixed during all the simulation. In the original DLM network, the behavior is based on rate coding (averaged neuron activity over time is encoded). Here, we propose the Oscillatory Dynamic Link Matching algorithm (ODLM) that uses models of conventional spiking neurons and for which the coding is based on phase (place coding). We observe that the network is capable of performing motion analysis without optical flow computation and no additional signal processing should be made between layers unlike in [2] (translation, rotation, etc. between the patterns in the first and second layers can be seen as motion). More generally, our proposed network can solve the correspondence problem, and at the same time, performs the segmentation of the scene, which is in accordance with the Gestalt theory of perception.