122,139 research outputs found
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 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
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