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Pre-synaptic Lateral Inhibition Provides a Better Architecture for Self-Organising Neural Networks (1999)  (Make Corrections)  (2 citations)
Michael W. Spratling
Network: Computation in Neural Systems



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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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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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