Masayuki Kikuchi

Publications (7)6.47 Total impact

• Source

  Available from: Osamu Watanabe

  Article: Hierarchical integration of individual motions in locally paired-dot stimuli.

  Osamu Watanabe, Masayuki Kikuchi
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  ABSTRACT: Recent psychophysical studies suggest that there are two types of motion integration processes in human visual system, i.e., the local and the global integration process. The existence of the local integration process is suggested by the vector-average perception in locally paired-dot (LPD) stimuli. Here, we investigated the relationship between the two motion integration processes by measuring the signal detection thresholds in three corresponding stimuli: (1) standard random-dot kinematograms (RDKs), (2) LPD stimuli the individual dot motions of which were identical to those of RDKs, and (3) pairwise-averaged stimuli the individual dot motions of which corresponded to the vector-averages of locally paired motions in LPD stimuli. We found that the thresholds in LPD stimuli were similar to those in pairwise-averaged stimuli rather than in RDKs. In addition, when dots were paired appropriately, observers could detect coherent motions in LPD stimuli even if the proportions of signal dots were less than the detection thresholds in corresponding RDKs. These results suggest that the local and global integrations of individual motions are carried out hierarchically, and that the global motion perception in LPD stimuli does not depend on individual dot motions directly, but depends on locally integrated motions.
  Vision Research 02/2006; 46(1-2):82-90. · 2.41 Impact Factor
• Source

  Available from: stir.ac.uk

  Article: Symmetry axis extraction by a neural network.

  Kunihiko Fukushima, Masayuki Kikuchi
  Neurocomputing. 01/2006; 69:1827-1836.
• Source

  Available from: Osamu Watanabe

  Article: Nonlinearity of the population activity to transparent motion.

  Osamu Watanabe, Masayuki Kikuchi
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  ABSTRACT: How to represent transparent motion with neuronal populations is important problem for the theory of multiple motion detection. Previous models are based on the assumption that the population activity to transparent motion is proportional to a linear combination of the responses to individual motions. However, there is a possibility that the population activity becomes a nonlinear combination of each motion's component due to the interference, or cross-talk, between two moving patterns. Here we show the model analysis of how a neuronal population represents multiple motions with the spatiotemporal energy model. The model analysis indicates there is a special case that the interference leads to the nonlinearity in the population response, although the linear combination assumption is satisfied in general. This special case corresponds to locally-paired-dot (LPD) stimuli that produce no transparency. Computer simulations show that a simple model for motion detection fails to discriminate two overlapping motions in this case due to the nonlinearity in population responses, and this failure is similar to human perception in LPD stimuli. This result suggests that non-transparency perception in LPD stimuli is naturally explained by the nonlinear property of neuronal responses.
  Neural Networks 02/2005; 18(1):15-22. · 2.18 Impact Factor
• Conference Proceeding: Assignment of Figural Side to Contours Based on Symmetry, Parallelism, and Convexity.

  Masayuki Kikuchi, Kunihiko Fukushima
  Knowledge-Based Intelligent Information and Engineering Systems, 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003, Proceedings, Part II; 01/2003
• Conference Proceeding: Neural Network Model Completing Occluded Contour.

  Masayuki Kikuchi, Koichi Anada, Kunihiko Fukushima
  The Fifth International Conference on Neural Information Processing, ICONIP'R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings; 01/1998
• Article: Neural Network Model of the Visual System: Binding Form and Motion.

  KUNIHIKO FUKUSHIMA, MASAYUKI KIKUCHI
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  ABSTRACT: We propose a neural network model of the visual system of the brain which processes different kinds of attributes such as form and motion in parallel. The model has two separate channels: a channel processing form and a channel processing motion. Each channel has both forward and backward connections, and exhibits selective attention. The selective attention mechanism, however, does not work independently because of the interaction of the two channels, which occurs at their lower layers. Both channels always focus attention on the same object even when many objects are presented simultaneously to the input layer of the model. The model was simulated on a computer: several objects made of moving random dots were applied to the input layer. At first the model focused attention on one of the objects, and detected its form and motion. It then processed the rest of the objects in turn by switching attention. Copyright 1996 Elsevier Science Ltd.
  Neural networks: the official journal of the International Neural Network Society 12/1996; 9(8):1417-1427. · 1.88 Impact Factor
• Article: Invariant pattern recognition with eye movement: A neural network model

  Masayuki Kikuchi, Kunihiko Fukushima
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  ABSTRACT: We propose a neural network model of the 2D invariant pattern recognition including a mechanism of saccadic eye movement. The model extracts every spatial relation between two primitive features (feature-relation) from a stimulus. The mechanism of saccadic eye movement enables the network to obtain all feature-relations present in the stimulus. This set of feature-relations describe uniquely the shape of pattern independent of position, scale, and deformation. Recognition of the presented pattern is achieved by classifying the obtained set of feature-relations. The ability of the model is confirmed by computer-simulation.
  Neurocomputing.