Neocognitron

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A Neocognitron Network is an Deep Artificial Neural Network that is composed of S-cells and C-cells.



References

2018a

  • (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Neocognitron Retrieved:2018-7-22.
    • The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in the 1980s. It has been used for handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks. The neocognitron was inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in the visual primary cortex called simple cell and complex cell, and also proposed a cascading model of these two types of cells for use in pattern recognition tasks. The neocognitron is a natural extension of these cascading models. The neocognitron consists of multiple types of cells, the most important of which are called S-cells and C-cells. [1] The local features are extracted by S-cells, and these features' deformation, such as local shifts, are tolerated by C-cells. Local features in the input are integrated gradually and classified in the higher layers. [2] The idea of local feature integration is found in several other models, such as the LeNet model and the SIFT model. There are various kinds of neocognitron. [3] For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention. [4]
  1. Fukushima 1987, p. 83.
  2. Fukushima 1987, p. 84.
  3. Fukushima 2007
  4. Fukushima 1987, pp.81, 85

2015

  • (Liang & Hu, 2015) ⇒ Ming Liang, and Xiaolin Hu. (2015). “Recurrent Convolutional Neural Network for Object Recognition.” In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3367-3375.
    • QUOTE: ... CNN is a type of artificial neural network, which originates from neuroscience dating back to the proposal of the first artificial neuron in 1943 [34]. In fact, CNN, as well as other hierarchical models including Neocognitron [13] and HMAX [38], is closely related to Hubel and Wiesel’s findings about simple cells and complex cells in the primary visual cortex (V1)[23, 22]. All of these models have purely feed-forward architectures, which can be viewed as crude approximations of the biological neural network in the brain. Anatomical evidences have shown that recurrent connections ubiquitously exist in the neocortex, and recurrent synapses typically outnumber feed-forward and top-down (or feedback) synapses [6]. ...

2007

1988

1980