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Abstract: Unsupervised learning is an important property of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organising neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as ... (Update)
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BibTeX entry: (Update)
M. W. Spratling, "Pre-synaptic lateral inhibition provides a better architecture for self-organizing neural networks," Network: Computation in Neural Systems, vol. 10, pp. 285--301, 1999. http://citeseer.ist.psu.edu/spratling99presynaptic.html More
@article{ spratling99,
author = "M. W. Spratling",
title = "Pre-synaptic lateral inhibition provides a better architecture
for self-organising neural networks",
journal = "Network: Computation in Neural Systems",
volume = "10",
number = "4",
pages = "285--301",
year = "1999",
url = "citeseer.ist.psu.edu/spratling99presynaptic.html" }
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