Joachim Buhmann and Klaus Schulten.
Invariant pattern recognition by means of fast synaptic plasticity.
In IEEE International Conference on Neural Networks, San Diego,
California, July 24-27, 1988, volume 1, pp. 125-132, New York, 1988. The
Institute of Electrical and Electronics Engineers.
BUHM88A
A two layer neural system for shift-invariant pattern recognition is proposed. Model neurons are endowed with "physiological dynamics" involving membrane potentials and axonic spikes. Synapses between the two layers are plastic and change according to spike coincidences (Hebbian rules). The first neural network (encoder network) extracts features from a presented pattern and codes the neighborhood relationship of features by coincident activity of neurons. The second network (memory network) has stored several patterns. During recognition of a presented pattern the neural system establishes a strong projection between the first and the second layer, enhances activity in the set of those neurons, which represent the presented patterns, and suppresses activity of other neurons. Synaptic plasticity according to Hebbian rules allows to generate a projection which preserves feature neighborhood relationships. The recognition system is designed according to suggestions of v.d. Malsburg (1981).
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