Neuronal Synchrony: A Versatile Code for the Definition of Relations?

A typical scene contains many different objects that, because of the limited processing capacity of the visual system, compete for neural representation. The competition among multiple objects in visual cortex can be biased by both bot- tom-up sensory-driven mechanisms and top-down influences, such as selective atten- tion. Functional brain imaging studies reveal that, both in the absence and in the presence of visual stimulation, biasing signals due to selective attention can modulate neural activity in visual cortex in several ways. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals derives from a network of areas in frontal and parietal cortex.

/pdf/mechanisms-of-visual-attention-in-the-human-cortex-1mxnsqjpz6.pdf

Mechanisms of Visual Attention in the Human Cortex

https://www.allpsych.uni-giessen.de/julia/SS09/Reading/UngerleiderHaxby_currOpinion94.pdf

'What' and 'where' in the human brain.

The visual system, like all sensory systems, contains parallel pathways (see Stone 1 983). Recently, m uch emphasis has been placed on the relationship between two subcortical and two cortical pathways. It has been suggested that the cortical and subcortical pathways are continuous, so that distinct channels of information that arise in the retina remain segregated up to the highest levels of visual cortex. According to this view, the visual system comprises two largely independent subsystems that mediate different classes of visual behaviors. In this paper, we evaluate this proposal, which has far-reaching implications for our understanding of the functional organization of the visual system. The subcortical projection from the retina to cerebral cortex is strongly dominated by the two pathways (M and P pathways) that are relayed by the magnocellular and parvocellular subdivisions of the lateral geniculate nucleus (LGN) (see Shapley & Perry 1 986). The importance of these pathways is demonstrated by the fact that they include about 90% of the axons that leave the retinas (Silveira & Perry 1 99 1 ) and that little vision survives when both pathways are destroyed (Schiller et al 1 990a). The P and M pathways maintain their sharp anatomical segregation through the termination of the LGN projection in layer 4C of VI (striate cortex). The complex network of connections in primate extrastriate visual cor-

How parallel are the primate visual pathways

The discovery and analysis of cortical visual areas is a major accomplishment of visual neuroscience. In the past decade the use of noninvasive functional imaging, particularly functional magnetic resonance imaging (fMRI), has dramatically increased our detailed knowledge of the functional organization of the human visual cortex and its relation to visual perception. The fMRI method offers a major advantage over other techniques applied in neuroscience by providing a large-scale neuroanatomical perspective that stems from its ability to image the entire brain essentially at once. This bird's eye view has the potential to reveal large-scale principles within the very complex plethora of visual areas. Thus, it could arrange the entire constellation of human visual areas in a unified functional organizational framework. Here we review recent findings and methods employed to uncover the functional properties of the human visual cortex focusing on two themes: functional specialization and hierarchical processing.

The human visual cortex

Spectral properties of 129 cells in the V4 area of 5 macaque monkeys were studied quantitatively with narrow-band and broad-band colored lights. The large majority of cells exhibited some degree of wavelength sensitivity within their receptive fields. The half-bandwidth of the primary peak in the spectral-response curve was less than 50 nm for 72% of the cells; the mean half-bandwidth of these cells, 27 nm, is similar to that found for color-opponent ganglion cells and cells in the parvocellular dorsal lateral geniculate nucleus (dLGN). Contrast- response functions indicated that the narrow spectral tuning of these cells derived from cone opponent interactions. From comparison of receptive-field sizes, we suggest that a typical V4 neuron sums inputs that ultimately derive from several thousand ganglion or parvocellular dLGN cells. In spite of their wavelength sensitivity, most V4 cells had properties that would not fit some simple criteria for classification as “color selective.” First, few cells showed overt signs of color opponency, namely, on-inhibition or off-excitation to spectrally opponent wavelengths. Second, about 30% of the cells in V4 had spectral- response curves with 2 peaks. (The wavelength distribution of these second peaks was almost identical to that of primary peaks, and combinations of peak wavelengths were fairly random.) Third, most cells responded to white light; overall, the response to white light was about 60% of that to the best narrow-band or broad-band colored light. Similarly, most V4 cells gave at least a small response to all or nearly all of the different broad-band colored lights we presented. Therefore, a given V4 cell is very likely to respond to most of the colored or white surfaces in natural scenes. These combinations of response properties probably explain the widely divergent percentages of “color” cells reported in previous studies of V4. The most unusual spectral property we found in V4 was a large, spectrally sensitive surround outside the “classical receptive field” of most cells. Although stimulation of the surround by itself did not cause any response, surround stimulation could completely suppress the response to even the optimally colored stimulus in the receptive field. In general, the optimal wavelengths for receptive-field excitation and surround suppression were the same or nearly so. Thus, “color contrast” may be computed in V4. In some cases, contrasting wavelengths in the surround caused moderate enhancement of response to a receptive-field stimulus.(ABSTRACT TRUNCATED AT 400 WORDS)

https://www.jneurosci.org/content/jneuro/10/10/3369.full.pdf

Spectral properties of V4 neurons in the macaque

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Spectral properties of V4 neurons in the macaque