The construction of directionally selective units, and their use in the processing of visual motion, are considered. The zero crossings of ∇ 2 G(x, y) ∗ I(x, y) are located, as in Marr & Hildreth (1980). That is, the image is filtered through centre-surround receptive fields, and the zero values in the output are found. In addition, the time derivative ∂[∇ 2 G(x, y) ∗ l(x, y) ]/∂ t is measured at the zero crossings, and serves to constrain the local direction of motion to within 180°. The direction of motion can be determined in a second stage, for example by combining the local constraints. The second part of the paper suggests a specific model of the information processing by the X and Y cells of the retina and lateral geniculate nucleus, and certain classes of cortical simple cells. A number of psychophysical and neurophysiological predictions are derived from the theory.


1992 ◽  
Vol 31 (22) ◽  
pp. 4534 ◽  
Author(s):  
Fatima Abdellani ◽  
Georges Rasigni ◽  
Monique Rasigni ◽  
Antoine Llebaria

Author(s):  
C. E. Carr

The weakly electric fish Eigenmannia is able to detect temporal disparities as small as 400 nanoseconds between two signals from different parts of the body surface. The elements of this time comparison circuit have been identified by EM reconstruction of its component cells.Information about the timing of the zero-crossings of signals on each area of the body surface is coded by phase coder receptors, a subset of tuberous electroreceptors. Electroreceptors on the body surface are innervated by primary afferents with a central termination on the spherical cells of the medullary electrosensory lateral line lobe. These cells project to lamina IV of the midbrain torus, a structure similar to the inferior colliculus. Afferents entering lamina VI form a very restricted terminal arbor in which they synapse upon the three cell types of this lamina.


2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.


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