TY - GEN
T1 - Divisive normalization
T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
AU - Lyu, Siwei
PY - 2010
Y1 - 2010
N2 - Divisive normalization (DN) has been advocated as an effective nonlinear efficient coding transform for natural sensory signals with applications in biology and engineering. In this work, we aim to establish a connection between the DN transform and the statistical properties of natural sensory signals. Our analysis is based on the use of multivariate t model to capture some important statistical properties of natural sensory signals. The multivariate t model justifies DN as an approximation to the transform that completely eliminates its statistical dependency. Furthermore, using the multivariate t model and measuring statistical dependency with multi-information, we can precisely quantify the statistical dependency that is reduced by the DN transform. We compare this with the actual performance of the DN transform in reducing statistical dependencies of natural sensory signals. Our theoretical analysis and quantitative evaluations confirm DN as an effective efficient coding transform for natural sensory signals. On the other hand, we also observe a previously unreported phenomenon that DN may increase statistical dependencies when the size of pooling is small.
AB - Divisive normalization (DN) has been advocated as an effective nonlinear efficient coding transform for natural sensory signals with applications in biology and engineering. In this work, we aim to establish a connection between the DN transform and the statistical properties of natural sensory signals. Our analysis is based on the use of multivariate t model to capture some important statistical properties of natural sensory signals. The multivariate t model justifies DN as an approximation to the transform that completely eliminates its statistical dependency. Furthermore, using the multivariate t model and measuring statistical dependency with multi-information, we can precisely quantify the statistical dependency that is reduced by the DN transform. We compare this with the actual performance of the DN transform in reducing statistical dependencies of natural sensory signals. Our theoretical analysis and quantitative evaluations confirm DN as an effective efficient coding transform for natural sensory signals. On the other hand, we also observe a previously unreported phenomenon that DN may increase statistical dependencies when the size of pooling is small.
UR - https://www.scopus.com/pages/publications/85162063480
M3 - Conference contribution
SN - 9781617823800
T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
BT - Advances in Neural Information Processing Systems 23
PB - Neural Information Processing Systems
Y2 - 6 December 2010 through 9 December 2010
ER -