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Pre-integration lateral inhibition enhances unsupervised learning

By M. W. Spratling and M. H. Johnson


A large and influential class of neural network architectures use post-integration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through pre-integration lateral inhibition, does provide appropriate coding properties and can be used to efficiently learn such representations. Furthermore, this architecture is consistent with both neuro-anatomical and neuro-physiological data. We thus argue that pre-integration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible

Topics: Neural Modelling, Computational Neuroscience, Neural Nets
Year: 2002
OAI identifier:

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  10. (1990). Forming sparse representations by local anti-Hebbian learning.
  11. (1991). Learning invariance from transformation sequences.
  12. (1997). A simple algorithm that discovers efficient perceptual codes.
  13. (1997). A neural net for PCA and beyond.
  14. (1976). Adaptive pattern classification and universal recoding, I: parallel development and coding of neural feature detectors.
  15. (1978). A theory of human memory: self-organisation and performance of sensory-motor codes, maps, and plans.
  16. (1987). Competitive learning: from interactive activation to adaptive resonance.
  17. (1998). Towards a biophysically plausible bidirectional Hebbian rule.
  18. (1996). Development of low entropy coding in a recurrent network.
  19. (1994). A fast method for activating competitive self-organising neural networks.
  20. (2001). Synaptic function: dendritic democracy. Current Biology,
  21. (2000). Diversity and dynamics of dendritic signalling.
  22. (1991). Introduction to the Theory of Neural Computation.
  23. (1995). The wake-sleep algorithm for unsupervised neural networks.
  24. (1997). Generative models for discovering sparse distributed representations.
  25. (1999). Feature extraction through LOCOCODE.
  26. (1995). Inhibitory control of excitable dendrites in neocortex.
  27. (1983). Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing.
  28. (2000). The role of single neurons in information processing.
  29. (1997). Self-Organizing Maps.
  30. (1995). Adaptive perceptual pattern recognition by self-organizing neural networks: context, uncertainty, multiplicity, and scale.
  31. (1998). Complex microstructures of sensory cortical connections. Current Opinion in Neurobiology,
  32. (1985). Feature discovery by competitive learning.
  33. (1989). Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks,
  34. (1995). A multiple cause mixture model for unsupervised learning.
  35. (1995). Dendritic processing.
  36. (1998). Excitable dendrites and spines: earlier theoretical insights elucidate recent direct observations. Trends in
  37. (1977). Storing covariance with nonlinearly interacting neurons.
  38. (1987). Logic operations are properties of computer-simulated interactions between excitable dendritic spines.
  39. (1994). Cooperative self-organization of afferent and lateral connections in cortical maps.
  40. (1985). Cortical circuitry underlying inhibitory processes in cat area 17.

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