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From Cats to Convolutional Neural Networks

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields.

The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors.

The first neural network model that drew inspiration from the hierarchy model of the visual nervous system proposed by Hubel and Wiesel was Neocognitron invented by Kunihiko Fukushima, and had the ability of performing unsupervised learning. Kunihiko Fukushima’s approach was commendable as it was the first neural network model having the capability of pattern recognition similar to human brain. The model gave a lot of insight and helped future understanding of the brain.

A successful advancement in Neocognitron was achieved by Yann LeCun and his team for isolated character recognition. The Convolutional Neural Network architecture was called LeNet-5 and it turned out much efficient than other classifiers. Today, in image classification, CNNs are preferred neural networks due to their in built feature learning mechanism.

CNNs have achieved the lowest error rate of 0.23 on MNIST database, a 34% improvement compared with networks [4]. CNNs are widely used in facial and object recognition.

CNNs have come a long way since the cat experiment, and are contributing a lot to make machines intelligent. Today CNNs are used in numerous applications including document recognition, object recognition, video surveillance, face detection.

References:

  1. Hubel, D. and Wiesel, T. (1959): Receptive fields of Single Neurones In The Cat’s Striate Cortex. Journal of Physiology, 195, 574-591.
  2. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.
  3. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. (November, 1998): Gradient-based learning applied to document recognition. Proceedings of the IEEE.
  4. Ciresan, D.; Meier, U.; Schmidhuber, J., "Multi-column deep neural networks for image classification," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on , vol., no., pp.3642-3649, 16-21 June 2012
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  1. The data newsletter20 January 2022 at 10:36

    I generally check this kind of article and I found your article which is related to my interest.sequence to sequence learning with neural networks Genuinely it is good and instructive information. Thankful to you for sharing an article like this.

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