A spiking neural network for tactile form based object recognition

  • Ratnasingam S
  • McGinnity T
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This paper proposes a biologically plausible system for object recognition based on tactile form perception. A spiking neural network, an encoding scheme for converting the input values into spike trains, a method for converting the output spike pattern into reliable features for object recognition and a training approach for the spiking neural network are proposed. Three separate spiking neural networks are used in this recognition system. Three features, based on the output firing pattern of the three networks, are projected onto a three dimensional space. Each class of objects forms a cluster in the three-dimensional feature space. During the training the firing threshold of the hidden layer is modified in such a way that the cluster formed by an object is small and does not overlap with neighbouring clusters. The system has been tested with a number of objects for recognition based on shape. In addition, the system has also been tested for the ability to recognise objects of the same shape but different size. The results show the proposed system gives good performance in recognising objects based on tactile form perception.

Author-supplied keywords

  • Tactile object recognition
  • robotic object recognition
  • tactile form perception

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  • Sivalogeswaran Ratnasingam

  • T.M. McGinnity

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