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Hiroshi Tsujino

Publications and source records attributed to Hiroshi Tsujino.

3 recordsLinked to original sources

A computational model of monkey cortical grating cells.

Grating cells were discovered in the V1 and V2 areas of the monkey visual cortex by von der Heydt et al. (1992). These cells responded vigorously to grating patterns of appropriate orientation and periodicity. Computational models inspired by these findings were used as texture operator (Kruzinga and Petkov 1995, 1999; Petkov and Kruzinga 1997) and for the emergence and self-organization of grating cells (Brunner et al. 1998; Bauer et al. 1999). The aim of this paper is to create a grating cell operator that demonstrates similar responses to monkey grating cells by applying operator to the same stimuli as in the experiments carried out by von der Heydt et al. (1992). Operator will be tested on images that contain periodic patterns as suggested by De Valois (1988). In order to learn more about the role of grating cells in natural vision, operator is applied to 338 real-world images of textures obtained from three different databases. The results suggest that grating cells respond strongly to regular alternating periodic patterns of a certain orientation. Such patterns are common in images of human-made structures, like buildings, fabrics, and tiles, and to regular natural periodic patterns, which are relatively rare in nature.

Animals↗

Output-driven operation and memory-based architecture principles embedded in a real-world device.

Two principles of neurocomputational design are implemented into an autonomous real-world device, such as a helicopter. The helicopter has a motivational component towards emitting motor responses in a manner similar to a fledging bird. We expect these two principles, together with an understanding of integrative brain activity and memory in which functionally and selectively distributed neural networks operate in vivo will eventually lead to the embodiment of cognition in a brain-like computer as an engineering counterpart of a real brain.

Aircraft↗

A Cortical-type Modular Neural Network for Hypothetical Reasoning.

We propose a multilayer neural network architecture that can implement the kind of hypothetical reasoning that the cortex seems to perform in making sense of the sensory input. The elementary processing nodes of each homogeneous sheet are not single formal neurons, but complex modules abducted from the functional organization of neocortical columns. As an example, we simulate face recognition in this neocortical architecture. A holistic but coarse initial hypothesis is generated by express forward input description and subsequently refined under the constraints of this hypothesis. Separation of forward input description and feedback generated hypothesis, while using the difference in both descriptions at each of the modular units to control the refinement, enables robust recognition and has the potential for autonomous learning. Copyright 1997 Elsevier Science Ltd.

Journal Article↗