Retinal location and structure in squid rhodopsin

In order to understand retinal we calculated the dihedral angles around carbon axis IOC-12C, since two different carbon sequences 9C-10C-11C-12C and 10C-11C-12C-13C exist. We also calculated the distances between two specified carbon pairs. Those results are tabulated. Photon absorption changes the conformation of retinal conformation. This fact is confirmed from dihedral angle changes and distance changes of targeted of retinal carbon atoms. These matters are discussed in the present paper.Copyright Information: Copyright the autho

Lessons from biological processing of image texture

When designing artificial vision systems, it may be useful to examine the solutions 0.5 billion years of biological evolution have produced. Recent studies of human vision; studies of macaque visual cortical function; and behavioural studies of bee vision, all indicate that different species have evolved related approaches for discriminating image textures. This common strategy uses short-range 4th-order spatial correlations. Isotrigon textures, ensemble averages of which have 3rd-order correlation functions that are equal to 0, are useful for studying this sense. Recent results from humans and bees, and methods for producing new isotrigon textures are described

Nearest neighbour coupled systems of four 1-D oscillators

A one-dimensional (1-D) map oscillator is used to investigate the network properties of connected oscillators. The oscillators are connected as nearest neighbours, like Purkinje cells. There are two kinds of oscillators, p-type and n-type. The two types correspond to excitatory and nhibitory neurons. Thus, the 1-D oscillator network becomes an artificial system based on knowledge from the brain

Geometrical characterization of textures consisting of two or three discrete colorings

Geometrical characterization for discretized contrast textures is realized by computing the Gaussian and mean curvatures relative to the central pixel of a clique and four neighboring pixels, these four neighbors either being first or second order neighbors. Practical formulae for computing these curvatures are presented. Curvatures based on the central pixel depend upon the brightness configuration of the clique pixels. Therefore the cliques are classified into classes by configuration of pixel contrast or coloring. To look at the textures formed by geometrically classified cliques, we create several textures using overlapping tiling of cliques belonging to a single curvature class. Several examples of hyperbolic textures, consisting of repeated hyperbolic cliques surrounded by non-hyperbolic cliques, are presented with the nonhyperbolic textures. We also introduce a system of 81 rotationally and brightness shift invariant geo-cliques that have shared curvatures and show that histograms of these 81 geo-cliques seem to be able to distinguish isotrigon textures

Spatial biases and computational constraints on the encoding of complex local image structure

The decomposition of visual scenes into elements described by orientation and spatial frequency is well documented in the early cortical visual system. How such 2nd-order elements are sewn together to create perceptual objects such as corners and intersections remains relatively unexplored. The current study combines information theory with structured deterministic patterns to gain insight into how complex (higher-order) image features are encoded. To more fully probe these mechanisms, many subjects (N = 24) and stimuli were employed. The detection of complex image structure was studied under conditions of learning and attentive versus preattentive visual scrutiny. Strong correlations (R2 > 0.8, P < 0.0001) were found between a particular family of spatially biased measures of image information and human sensitivity to a large range of visual structures. The results point to computational and spatial limitations of such encoding. Of the extremely large set of complex spatial interactions that are possible, the small subset perceivable by humans were found to be dominated by those occurring along sets of one or more narrow parallel lines. Within such spatial domains, the number of pieces of visual information (pixel values) that may be simultaneously considered is limited to a maximum of 10 points. Learning and processes involved in attentive scrutiny do little if anything to increase the dimensionality of this system

Atomic configuration around retinal in squid rhodopsin

Distances among retinal carbons and amino acid residues around a retinal molecule are calculated. We draw an image for chain-A and chain-B contact with squid retinal. Thus the area of each retinal's chain-A and chain-B is decided in an average sense that is not exact. We discuss about distances and determined areas of retinals

A quantum chemical study of the retinal of squid rhodopsin

The squid retinal structure obtained from the Protein Data Bank (PDB ids 2Z73 and 2ZIY) is studied in a quantum chemistry method using MOPAC2009 based on a semi-empirical method with PM6 parametrization. The interaction between retinal and light (an electromagnetic field) is effectively described by the interaction between dipoles and electromagnetic fields. Thus we investigated the dipole moment and effective charge distribution of retinal. We also looked at molecular orbitals, especially the HOMO (highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital). MO shifting between a double bonded site and single bonded site is seen by comparing HOMO and LUMO results. Retinal changes its conformation from cis to trans at carbon 11. This carbon's effective charge is very small so that it is free from electric interactions. Then it can change conformation with a small change in energy