60 research outputs found

    New (aminomethyl)phosphines via selective hydrophosphination and/or phosphorus based Mannich condensation reactions

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    Controlled stepwise reaction of a geminal substituted alkene or primary amine group afforded a small library of new functionalised tertiary and ditertiary phosphines. Accordingly, Mannich based condensation of the commercially available disubstituted arene C 6 H 4 (NH 2 ){2-C(Me)=CH 2 } with HOCH 2 PR 2 (R 2 = Cg: 1,3,5,7-tetramethyl-2,4,8-trioxa-6-phosphaadamantyl; Ph 2 ) afforded the (aminomethyl)phosphines C 6 H 4 (NHCH 2 PCg){2-C(Me)=CH 2 } L 1 and C 6 H 4 (NHCH 2 PPh 2 ){2-C(Me)=CH 2 } L 2 in approx. 60% yield. In addition to the formation of L 2 , the diphosphine L 3 was also identified and independently synthesised upon reaction of C 6 H 4 (NH 2 ){2-C(Me)=CH 2 } with two equiv. of HOCH 2 PPh 2 in CH 3 OH under reflux. Alternatively, reaction of C 6 H 4 (NH 2 ){2-C(Me)=CH 2 } with H-PR 2 (R 2 = Cg or Ph 2 ) in the presence of AIBN [2,2′-azobis(2-methylpropionitrile)] as free radical initiator, afforded the primary amine functionalised phosphines C 6 H 4 (NH 2 ){2-CH(Me)CH 2 PCg} L 4 and C 6 H 4 (NH 2 ){2-CH(Me)CH 2 PPh 2 } L 5 in 85% and 66% isolated yields respectively. In both cases only the anti-Markovnikov addition products were observed. Subsequent reaction of L 5 with HOCH 2 PR 2 (R 2 = Ph 2 ) afforded the unsymmetrical ditertiary phosphine C 6 H 4 (NHCH 2 PPh 2 ){2-CH(Me)CH 2 PPh 2 } L 6 . Some preliminary coordination studies towards [RuCl(μ-Cl)(η 6 -C 10 H 14 )] 2 [AuCl(tht)] (tht = tetrahydrothiophene) and [MCl 2 (η 4 -cod)] (M = Pd, Pt; cod = cycloocta-1,5-diene) demonstrate these new ligands behave as classic P-donors leaving the pendant amino or alkenyl groups non-coordinating. All compounds have been characterised by multinuclear NMR, FT−IR, mass spectrometry and microanalysis. Single crystal X-ray studies have been performed on L 3 , L 5 , L 6 , 1, 3b·0.5CH 2 Cl 2 , 4a·1.5CH 2 Cl 2 , 5 and 6·0.5CDCl 3 ·0.5C 4 H 10 O

    New tunable metal phosphine complexes with polymerisation capabilities

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    New tunable metal phosphine complexes with polymerisation capabilitie

    Screen and retinal images during the pursuit eye movement.

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    <p>The subject performs pursuit eye movements upon a target moving horizontally to the left on a screen with static background dots (a). On the retinal image during the pursuit, the target is relatively stable on the fovea, while the “static” dots are induced to move toward the right (b). Note that the normal inversion of the visual field on the retina is ignored in this diagram for the sake of clarifying the trade-off of retinal vs. screen motion during pursuit eye movement.</p

    Tuning Properties of MT and MSTd and Divisive Interactions for Eye-Movement Compensation

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    <div><p>The primate brain intelligently processes visual information from the world as the eyes move constantly. The brain must take into account visual motion induced by eye movements, so that visual information about the outside world can be recovered. Certain neurons in the dorsal part of monkey medial superior temporal area (MSTd) play an important role in integrating information about eye movements and visual motion. When a monkey tracks a moving target with its eyes, these neurons respond to visual motion as well as to smooth pursuit eye movements. Furthermore, the responses of some MSTd neurons to the motion of objects in the world are very similar during pursuit and during fixation, even though the visual information on the retina is altered by the pursuit eye movement. We call these neurons compensatory pursuit neurons. In this study we develop a computational model of MSTd compensatory pursuit neurons based on physiological data from single unit studies. Our model MSTd neurons can simulate the velocity tuning of monkey MSTd neurons. The model MSTd neurons also show the pursuit compensation property. We find that pursuit compensation can be achieved by divisive interaction between signals coding eye movements and signals coding visual motion. The model generates two implications that can be tested in future experiments: (1) compensatory pursuit neurons in MSTd should have the same direction preference for pursuit and retinal visual motion; (2) there should be non-compensatory pursuit neurons that show opposite preferred directions of pursuit and retinal visual motion.</p></div

    An example of a predicted compensatory pursuit neuron with similar velocity tuning of pursuit and visual motion.

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    <p>The black dots and line show the tuning and corresponding fit of visual motion, while the gray open dots and line show those of pursuit. Adapted from Churchland and Lisberger (2005).</p

    Model summary.

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    <p>The MSTd computation contains five stages: 1) calculate the summation of MT responses and calculate the exponential form of the MT summation, 2) define pursuit input as a function of pursuit velocity with the mirrored visual tuning function (see text), 3) calculate the result of divisive interaction between the MT and pursuit responses, 4) use the result as the input to a simple shunting computation and 5) solve the shunting equation in its equilibrium state.</p

    Pursuit compensation of MSTd neurons.

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    <p>MT neurons are tuned to retinal velocity. Thus, for different eye movement velocities, the velocity tuning curves of MT neurons shift away from the velocity tuning curve for the fixation condition in screen coordinates (c and d). Unlike MT neurons, MSTd neurons can compensate for the visual motion induced by pursuit to represent the real velocity on screen rather than the retinal velocity. The compensation is indicated by the overlapping velocity tuning curves in the screen reference frame for different eye movement velocities (a), as well as the shifting of the same curves in the retinal frame (b). The shifting distance of each curve depends on the pursuit speed. For a perfect compensation, the shift distance should be equal to the speed of the pursuit but in the opposite direction. Different colors represent different pursuit velocities as shown in the legends in (a). Adapted from Inaba et al. (2011).</p

    Direction-selective responses of the MSTd neuron shown in Fig 3 to the background motion on the screen during fixation (black arrows/lines) and pursuit in the preferred direction (red arrows/lines).

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    <p><b>(a)</b> Motion on retina as a result of pursuit. Each arrow represents the motion of the background toward one of the eight directions. For the same background motion on screen for the fixation (black arrows) and pursuit conditions (red arrows), the motion on retina during pursuit is different from those during fixation. <b>(b)</b> The MSTd neuron shows similar direction tuning with or without pursuit. Our modeled neuron also shows similar direction tuning to the MSTd neuron during fixation (black line) and pursuit to preferred, preferred+90 degrees, anti-preferred, and anti-preferred+90 degrees direction (red, cyan, green and blue lines, respectively). (a) and the left plot in (b) are adapted from Inaba et al. (2011).</p

    An MSTd cell that responds to both a large field dots moving to the right and to pursuit of a target moving to the left on a dark background.

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    <p>The height of the gray vertical bar indicates 250 spikes per second. Adapted from Komatsu and Wurtz (1988).</p

    The log-Gaussian function of MT velocity tuning and the input to the model MSTd neuron.

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    <p>(a) Three examples of the velocity tuning curves of model MT neurons with the preferred velocity parameter <i>μ</i> set at 10, 20 and 30 degrees per second. (b) The summing weights follow a power function of preferred speed with a negative power. The response is assumed to be negative when the preferred direction of MT neurons is opposite to their projecting MSTd neuron. In each plot, the x-axis is the retinal velocity in degrees per second, and the y-axis is unit-less since the responses are normalized.</p
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