One-Shot Learning with Feedback for Multi-layered Convolutional Network

  • Kunihiko Fukushima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


This paper proposes an improved add-if-silent rule, which is suited for training intermediate layers of a multi-layered convolutional network, such as a neocognitron. By the add-if-silent rule, a new cell is generated if all postsynaptic cells are silent. The generated cell learns the activity of the presynaptic cells in one-shot, and its input connections will never be modified afterward. To use this learning rule for a convolutional network, it is required to decide at which retinotopic location this rule is to be applied. In the conventional add-if-silent rule, we chose the location where the activity of presynaptic cells is the largest. In the proposed new learning rule, a negative feedback is introduced from postsynaptic cells to presynaptic cells, and a new cell is generated at the location where the presynaptic activity fails to be suppressed by the feedback. We apply this learning rule to a neocognitron for hand-written digit recognition, and demonstrate the decrease in the recognition error.


add-if-silent one-shot learning negative feedback neocognitron convolutional network pattern recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hinton, G.E., Osindero, S., Teh, Y.: A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18, 1527–1554 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Fukushima, K.: Training Multi-layered Neural Network Neocognitron. Neural Networks 40, 18–31 (2013)CrossRefzbMATHGoogle Scholar
  3. 3.
    Fukushima, K.: Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biol. Cybernetics 36(4), 193–202 (1980)CrossRefzbMATHGoogle Scholar
  4. 4.
    Fukushima, K.: Artificial Vision by Multi-layered Neural Networks: Neocognitron and its Advances. Neural Networks 37, 103–119 (2013)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Fukushima, K.: Interpolating Vectors for Robust Pattern Recognition. Neural Networks 20(8), 904–916 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
  7. 7.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kunihiko Fukushima
    • 1
  1. 1.Fuzzy Logic Systems InstituteIizukaJapan

Personalised recommendations