12 research outputs found
Attentional modulation of firing rate and synchrony in a model cortical network
When attention is directed into the receptive field of a V4 neuron, its
contrast response curve is shifted to lower contrast values (Reynolds et al,
2000, Neuron 26:703). Attention also increases the coherence between neurons
responding to the same stimulus (Fries et al, 2001, Science 291:1560). We
studied how the firing rate and synchrony of a densely interconnected cortical
network varied with contrast and how they were modulated by attention. We found
that an increased driving current to the excitatory neurons increased the
overall firing rate of the network, whereas variation of the driving current to
inhibitory neurons modulated the synchrony of the network. We explain the
synchrony modulation in terms of a locking phenomenon during which the ratio of
excitatory to inhibitory firing rates is approximately constant for a range of
driving current values. We explored the hypothesis that contrast is represented
primarily as a drive to the excitatory neurons, whereas attention corresponds
to a reduction in driving current to the inhibitory neurons. Using this
hypothesis, the model reproduces the following experimental observations: (1)
the firing rate of the excitatory neurons increases with contrast; (2) for high
contrast stimuli, the firing rate saturates and the network synchronizes; (3)
attention shifts the contrast response curve to lower contrast values; (4)
attention leads to stronger synchronization that starts at a lower value of the
contrast compared with the attend-away condition. In addition, it predicts that
attention increases the delay between the inhibitory and excitatory synchronous
volleys produced by the network, allowing the stimulus to recruit more
downstream neurons.Comment: 36 pages, submitted to Journal of Computational Neuroscienc
Quantum transport properties of ultrathin silver nanowires
The quantum transport properties of the ultrathin silver nanowires are
investigated. For a perfect crystalline nanowire with four atoms per unit cell,
three conduction channels are found, corresponding to three bands crossing
the Fermi level. One conductance channel is disrupted by a single-atom defect,
either adding or removing one atom. Quantum interference effect leads to
oscillation of conductance versus the inter-defect distance. In the presence of
multiple-atom defect, one conduction channel remains robust at Fermi level
regardless the details of defect configuration. The histogram of conductance
calculated for a finite nanowire (seven atoms per cross section) with a large
number of random defect configurations agrees well with recent experiment.Comment: 4 pages, 6 figure
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Recognition of objects from partial information presents a significant
challenge for theories of vision because it requires spatial integration and
extrapolation from prior knowledge. We combined neurophysiological recordings
in human cortex with psychophysical measurements and computational modeling to
investigate the mechanisms involved in object completion. We recorded
intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to
measure the timing and selectivity of responses along human visual cortex to
whole and partial objects. Responses along the ventral visual stream remained
selective despite showing only 9-25% of the object. However, these visually
selective signals emerged ~100 ms later for partial versus whole objects. The
processing delays were particularly pronounced in higher visual areas within
the ventral stream, suggesting the involvement of additional recurrent
processing. In separate psychophysics experiments, disrupting this recurrent
computation with a backward mask at ~75ms significantly impaired recognition of
partial, but not whole, objects. Additionally, computational modeling shows
that the performance of a purely bottom-up architecture is impaired by heavy
occlusion and that this effect can be partially rescued via the incorporation
of top-down connections. These results provide spatiotemporal constraints on
theories of object recognition that involve recurrent processing to recognize
objects from partial information
Quantum Interference Effects in Electronic Transport through Nanotube Contacts
Quantum interference has dramatic effects on electronic transport through
nanotube contacts. In optimal configuration the intertube conductance can
approach that of a perfect nanotube (). The maximum conductance
increases rapidly with the contact length up to 10 nm, beyond which it exhibits
long wavelength oscillations. This is attributed to the resonant cavity-like
interference phenomena in the contact region. For two concentric nanotubes
symmetry breaking reduces the maximum intertube conductance from to
. The phenomena discussed here can serve as a foundation for building
nanotube electronic circuits and high speed nanoscale electromechanical
devices
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Robust Selectivity to Two-Object Images in Human Visual Cortex
We can recognize objects in complex images in a fraction of a second. Neuronal responses in macaque areas V4 and inferior temporal cortex to preferred stimuli are typically suppressed by the addition of other objects within the receptive field (see, however, [16, 17]). How can this suppression be reconciled with rapid visual recognition in complex scenes? Certain "special categories" could be unaffected by other objects, but this leaves the problem unsolved for other categories. Another possibility is that serial attentional shifts help ameliorate the problem of distractor objects. Yet, psychophysical studies, scalp recordings, and neurophysiological recordings suggest that the initial sweep of visual processing contains a significant amount of information. We recorded intracranial field potentials in human visual cortex during presentation of flashes of two-object images. Visual selectivity from temporal cortex during the initial approximately 200 ms was largely robust to the presence of other objects. We could train linear decoders on the responses to isolated objects and decode information in two-object images. These observations are compatible with parallel, hierarchical, and feed-forward theories of rapid visual recognition and may provide a neural substrate to begin to unravel rapid recognition in natural scenes