A feedback model of visual attention

Feedback connections are a prominent feature of cortical anatomy and are likely to have significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research

Exploring the functional significance of dendritic inhibition in cortical pyramidal cells

Inhibitory synapses contacting the soma and axon initial segment are commonly presumed to participate in shaping the response properties of cortical pyramidal cells. Such an inhibitory mechanism has been explored in numerous computational models. However, the majority of inhibitory synapses target the dendrites of pyramidal cells, and recent physiological data suggests that this dendritic inhibition affects tuning properties. We describe a model that can be used to investigate the role of dendritic inhibition in the competition between neurons. With this model we demonstrate that dendritic inhibition significantly enhances the computational and representational properties of neural networks

Pre-integration lateral inhibition enhances unsupervised learning

A large and influential class of neural network architectures use post-integration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through pre-integration lateral inhibition, does provide appropriate coding properties and can be used to efficiently learn such representations. Furthermore, this architecture is consistent with both neuro-anatomical and neuro-physiological data. We thus argue that pre-integration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible

Dendritic inhibition enhances neural coding properties.

The presence of a large number of inhibitory contacts at the soma and axon initial segment of cortical pyramidal cells has inspired a large and influential class of neural network model which use post-integration lateral inhibition as a mechanism for competition between nodes. However, inhibitory synapses also target the dendrites of pyramidal cells. The role of this dendritic inhibition in competition between neurons has not previously been addressed. We demonstrate, using a simple computational model, that such pre-integration lateral inhibition provides networks of neurons with useful representational and computational properties which are not provided by post-integration inhibition

An Extended Isgur-Paton Model: Agreement With the Lattice?

The spectrum for the pure gauge sector is calculated for an extended Isgur-Paton model in 2+1 and 3+1 dimensions and compared to recent lattice calculations of the glueball spectrum. The IP model is extended by inclusion of a rigidity (curvature) term and, in D=2+1, mixing through a higer topological contribution. For a choice of parameterizations, near quantitative agreement is found for SU(3) in D=2+1, but in D=3+1 the extensions fail to remedy the qualitative disagreement.Comment: 3 pages, LaTeX2e, uses espcrc2.sty, 2 eps figures included, talk given at LATTICE9

Excitation energies, polarizabilities, multipole transition rates, and lifetimes of ions along the francium isoelectronic sequence

Relativistic many-body perturbation theory is applied to study properties of ions of the francium isoelectronic sequence. Specifically, energies of the 7s, 7p, 6d, and 5f states of Fr-like ions with nuclear charges Z = 87 - 100 are calculated through third order; reduced matrix elements, oscillator strengths, transition rates, and lifetimes are determined for 7s - 7p, 7p - 6d, and 6d - 5f electric-dipole transitions; and 7s - 6d, 7s - 5f, and 5f_5/2 - 5f_7/2 multipole matrix elements are evaluated to obtain the lifetimes of low-lying excited states. Moreover, for the ions Z = 87 - 92 calculations are also carried out using the relativistic all-order single-double method, in which single and double excitations of Dirac-Fock wave functions are included to all orders in perturbation theory. With the aid of the SD wave functions, we obtain accurate values of energies, transition rates, oscillator strengths, and the lifetimes of these six ions. Ground state scalar polarizabilities in Fr I, Ra II, Ac III, and Th IV are calculated using relativistic third-order and all-order methods. Ground state scalar polarizabilities for other Fr-like ions are calculated using a relativistic second-order method. These calculations provide a theoretical benchmark for comparison with experiment and theory.Comment: 13 figures, 11 table

Excitation energies, polarizabilities, multipole transition rates, and lifetimes in Th IV

Excitation energies of the ns_{1/2} (n=7-10), np_j (n=7-9), nd_j (n=6-8), nf_{j} (n=5-7), and ng_{j} (n=5-6) states in Th IV are evaluated. First-, second-, third-, and all-order Coulomb energies and first- and second-order Coulomb-Breit energies are calculated. Reduced matrix elements, oscillator strengths, transition rates, and lifetimes are determined for the 96 possible nl_j-n'l'_j' electric-dipole transitions. Multipole matrix elements (7s_{1/2}-6d_j, 7s_{1/2}-5f_j, and 5f_{5/2}-5f_{7/2}) are evaluated to obtain the lifetimes of the $5f_{7/2}$ and 7s_{1/2}$ states. Matrix elements are calculated using both relativistic many-body perturbation theory, complete through third order, and a relativistic all-order method restricted to single and double (SD) excitations. Scalar and tensor polarizabilities for the 5f_{5/2} ground state in Th3+ are calculated using relativistic third-order and all-order methods. These calculations provide a theoretical benchmark for comparison with experiment and theory.Comment: 9 pages, 9 figure

Surface flux pinning in superconducting amorphous (Mo0.6Ru0.4)B18

Superconducting critical current density was measured as a function of a perpendicular applied magnetic field in glassy (Mo0.6Ru0.4)82B18. The pinning force density was observed to depend linearly on 1/w, where w is the sample width measured perpendicular to both the current and field. This dependence is attributed to pinning by the sample edges. The bulk pinning contribution can be separated from the edge pinning contribution by extrapolation of the Fp vs 1/w curve. The edge contribution of the flux pinning was nearly eliminated by electrolytically polishing the sample. The contribution of the flux pinning profile due to edge pinning is analyzed in terms of the dynamic pinning model modified for edge pinning

A Profile of Frail Older Americans and Their Caregivers

Provides a profile of older Americans and their caregivers, focusing on people age 65 and older who are not in nursing homes, and those with severe disabilities. Includes policy implications and recommendations for community-based home care options

Neural coding strategies and mechanisms of competition

A long running debate has concerned the question of whether neural representations are encoded using a distributed or a local coding scheme. In both schemes individual neurons respond to certain specific patterns of pre-synaptic activity. Hence, rather than being dichotomous, both coding schemes are based on the same representational mechanism. We argue that a population of neurons needs to be capable of learning both local and distributed representations, as appropriate to the task, and should be capable of generating both local and distributed codes in response to different stimuli. Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements. In contrast, we present a neural network architecture which enables a single algorithm to efficiently learn, and respond using, both types of coding scheme