Dependence of V2 illusory contour response on V1 cell properties and topographic organization.

An illusory contour is an image that is perceived as a contour in the absence of typical contour characteristics, such as a change in luminance or chromaticity across the stimulus. In cats and primates, cells that respond to illusory contours are sparse in cortical area V1, but are found in greater numbers in cortical area V2. We propose a model capable of illusory contour detection that is based on a realistic topographic organization of V1 cells, which reproduces the responses of individual cell types measured experimentally. The model allows us to explain several experimentally observed properties of V2 cells including variability in orientation tuning and inducer spacing preference. As a practical application, the model can be used to estimate the relationship between the severity of a cortical injury in the primary visual cortex and the deterioration of V2 cell responses to real and illusory contours.

Spatiotemporal dynamics underlying object completion in human ventral visual cortex.

Natural vision often involves recognizing objects from partial information. Recognition of objects from parts presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. Here we recorded intracranial field potentials of 113 visually selective electrodes from epilepsy patients in response to whole and partial objects. Responses along the ventral visual stream, particularly the inferior occipital and fusiform gyri, remained selective despite showing only 9%-25% of the object areas. However, these visually selective signals emerged ∼100 ms later for partial versus whole objects. These processing delays were particularly pronounced in higher visual areas within the ventral stream. This latency difference persisted when controlling for changes in contrast, signal amplitude, and the strength of selectivity. These results argue against a purely feedforward explanation of recognition from partial information, and provide spatiotemporal constraints on theories of object recognition that involve recurrent processing.

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.

Spatial attention in area V4 is mediated by circuits in primary visual cortex.

The ability to covertly select visual stimuli in our environment based on their behavioral relevance is an important skill. Stimulus selection has been studied experimentally, at the single neuron as well as at the population level, by recording from the visual cortex of subjects performing attention-demanding tasks, but studies at the local circuit level are lacking. We conducted simulations of a primary visual cortex (V1) model to provide insight into the local circuit computation underlying stimulus selection in V4. Two small oriented rectangular bars were placed at different locations in the 4 by 4 degree visual field represented by the V1 model, such that they activated different V1 neurons but such that they were both inside the classical receptive field (CRF) of the same V4 neuron. The biased competition framework [Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193-222] makes predictions for the response of V4 neurons and the modulation thereof by spatial and feature attention. In our simulation of the V1 network, we obtained results consistent with these predictions for V4 when the model had long-range excitatory projections targeting inhibitory neurons and when spatial attention was mediated by a spatially restricted projection that either inhibited the inhibitory neurons or excited the excitatory neurons. Although it is not clear whether attention effects measured in V4 neurons are generated mostly by local circuits within V4, our simulations suggest that spatial attention at a resolution less than the size of the CRF of a V4 neuron is inherited from upstream areas like V1 and relies on circuits mediating surround suppression at the single neuron level. Furthermore, the model displayed global oscillations in the alpha frequency range (around 10 Hz), whose coherence was highest in the absence of visual stimulation, which is consistent with electroencephalograms recorded in humans. By contrast, when a stimulus was presented the alpha oscillation sped up and became less coherent, whereas at the single column level (40-480 cells) transient beta/gamma oscillations were observed with a frequency between 25 and 50 Hz.

Role of interneuron diversity in the cortical microcircuit for attention.

Receptive fields of neurons in cortical area V4 are large enough to fit multiple stimuli, making V4 the ideal place to study the effects of selective attention at the single-neuron level. Experiments have revealed evidence for stimulus competition and have characterized the effect thereon of spatial and feature-based attention. We developed a biophysical model with spiking neurons and conductance-based synapses. To account for the comprehensive set of experimental results, it was necessary to include in the model, in addition to regular spiking excitatory (E) cells, two types of interneurons: feedforward interneurons (FFI) and top-down interneurons (TDI). Feature-based attention was mediated by a projection of the TDI to the FFI, stimulus competition was mediated by a cross-columnar excitatory connection to the FFI, whereas spatial attention was mediated by an increase in activity of the feedforward inputs from cortical area V2. The model predicts that spatial attention increases the FFI firing rate, whereas feature-based attention decreases the FFI firing rate and increases the TDI firing rate. During strong stimulus competition, the E cells were synchronous in the beta frequency range (15-35 Hz), but with feature-based attention, they became synchronous in the gamma frequency range (35-50 Hz). We propose that the FFI correspond to fast-spiking, parvalbumin-positive basket cells and that the TDI correspond to cells with a double-bouquet morphology that are immunoreactive to calbindin or calretinin. Taken together, the model results provide an experimentally testable hypothesis for the behavior of two interneuron types under attentional modulation.

Attentional modulation of firing rate and synchrony in a model cortical network.

The response of a neuron in the visual cortex to stimuli of different contrast placed in its receptive field is commonly characterized using the contrast response curve. 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). The neuron will thus be able to respond to weaker stimuli than it responded to without attention. Attention also increases the coherence between neurons responding to the same stimulus (Fries et al., 2001). We studied how the firing rate and synchrony of a densely interconnected cortical network varied with contrast and how they were modulated by attention. The changes in contrast and attention were modeled as changes in driving current to the network neurons. 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.