Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network

Rao, A. Ravishankar; Cecchi, Guillermo A.

doi:10.1007/978-3-540-87559-8_71

Cited by 3 publications

(2 citation statements)

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“…Several interesting avenues can be explored with our model, including applying it to replicate delayed feedback responses in primate cortex to illusory contours, as demonstrated by Lamme et al (1998, 2001). We have been able to show such delayed feedback responses in a simulated multi‐layer oscillatory network, as presented in Rao and Cecchi (2008). Hence, our computational model provides a platform to conduct simulations with different network topologies, such as the extent and localization of inter‐unit connectivity.…”

Section: Discussionmentioning

confidence: 78%

“…Figure 16 shows that the separation accuracy does not depend on the feedback projection diameter. We probed this observation further in Rao and Cecchi (2008), and demonstrated that high‐level object categorization depends mainly on the feed‐forward pathway, and not on the feedback pathway, which in its turn contributes mainly to phase synchronization. This behavior was intuitively expected based on the construction of the objective function, and on the specific set of experiments that we performed.…”

Section: Discussionmentioning

confidence: 96%

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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: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 96%