High-Level Visual Processing

Fukushima, Tetsuya; Kasahara, Hironori; Kamigaki, Tsukasa; Miyashita, Yasushi

doi:10.1016/b978-012370880-9.00289-9

Cited by 1 publication

(1 citation statement)

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“…Fukushima et al (2008) observe that though neurons in the inferior temporal (IT) cortex can respond to moderately complex visual features, a single neuron cannot encode entire complex visual objects. Hence, it becomes necessary to combine the responses of multiple neurons for object representation.…”

Section: Introductionmentioning

confidence: 99%

An objective function utilizing complex sparsity for efficient segmentation in multi‐layer oscillatory networks

Rao

Cecchi

2010

International Journal of Intelligent Computing and Cybernetics

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PurposeThe purpose of this paper is to extend an analysis presented in earlier work which investigated the dynamical behavior of a network of oscillatory units described by the amplitude of and phase of oscillations, and to present an objective function that can be successfully applied to multi‐layer networks.Design/methodology/approachIn this paper, an objective function is presented that can be successfully applied to multi‐layer networks. The behavior of the objective function is explained through its ability to achieve a sparse representation of the inputs in complex‐valued space.FindingsIt is found that if the activity of each network unit is represented by a phasor in the complex plane, then sparsity is achieved when there is maximal phase separation in the complex plane. Increasing the spread of feedback connections is shown to improve segmentation performance significantly but does not affect separation performance. This enables a quantitative approach to characterizing and understanding cortical function.Originality/valueThe formulation of the multi‐layer objective function and the interpretation of its behavior through sparsity in complex space are novel contributions of this paper.

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Section: Introductionmentioning

confidence: 99%