When we see a person's face, we can easily recognize their species, individual identity and emotional state. How does the brain represent such complex information? A substantial number of neurons in the macaque temporal cortex respond to faces. However, the neuronal mechanisms underlying the processing of complex information are not yet clear. Here we recorded the activity of single neurons in the temporal cortex of macaque monkeys while presenting visual stimuli consisting of geometric shapes, and monkey and human faces with various expressions. Information theory was used to investigate how well the neuronal responses could categorize the stimuli. We found that single neurons conveyed two different scales of facial information in their firing patterns, starting at different latencies. Global information, categorizing stimuli as monkey faces, human faces or shapes, was conveyed in the earliest part of the responses. Fine information about identity or expression was conveyed later, beginning on average 51 ms after global information. We speculate that global information could be used as a 'header' to prepare destination areas for receiving more detailed information.

How does the brain represent objects in the world? A proportion of cells in the temporal cortex of monkeys responds specifically to objects, such as faces, but the type of coding used by these cells is not known. Population analysis of two sets of such cells showed that information is carried at the level of the population and that this information relates, in the anterior inferotemporal cortex, to the physical properties of face stimuli and, in the superior temporal polysensory area, to other aspects of the faces, such as their familiarity. There was often sufficient information in small populations of neurons to identify particular faces. These results suggest that representations of complex stimuli in the higher visual areas may take the form of a sparse population code.

We tested the color selectivity of neurons in the inferior temporal (IT) cortex of two awake macaque monkeys. A color stimulus was presented at the center of the visual field while the animal performed a fixation task. The responses of single units to various colors were recorded and were plotted in a color space. The color space was based on the CIE (Commission Internationale de l'Eclairage) chromaticity diagram. Quantitative analysis of the color selectivity was performed using a standard set of colors that were evenly distributed in the color space. Sixty-five neurons recorded from eight guide tubes implanted in anterior part of IT cortex were tested quantitatively, and their color selectivity was statistically evaluated. Forty-six of them (71%) were classified as color selective. Color-selective cells responded to some colors but not to others, and we called the responsive region in the color space the "color field." The location and the size of a cell's color field were largely independent of the luminance of the stimulus. About 80% of the color-selective neurons had color fields consisting of a single responsive region that were restricted in some part of the color space, and the remaining cells had more than one responsive region within the color space. Preferred hues of the color-selective neurons differed from cell to cell. The population of cells together covered nearly all of the color space. There was a tendency for more color-selective cells to be less sensitive to white and desaturated cyans. Some of the cells with color fields that consisted of more than one responsive region were more sensitive to saturation of the stimulus than to its hue. Although previous electrophysiological studies in IT cortex emphasized the pattern selectivity of the neurons, our results suggest that color is an important factor in the role that IT cortex plays in the object recognition.

1. We studied response properties of neurons in the superior temporal sulcus (STS) of behaving monkeys that discharged during brief, sudden movements of a large-field visual stimulus, eliciting ocular following. Most neurons responded to movements of a large-field visual stimulus with directional selectivity, preferring high stimulus speeds. Neurons were mostly recorded in the medial superior temporal area (MST) (187/250) and the middle temporal area (MT) (57/250). Further response properties were studied in the MST neurons. 2. Response latencies were measured when a large-field random dot pattern was moved in the preferred direction and preferred speed for each neuron. Eighty percent (120/150) of the neurons were activated < 50 ms after the onset of the stimulus motion. In most cases (89%, 134/150), increased firing rates started before the eye movements, with 59% (88/150) starting > 10 ms before the eye movements. 3. The relationship between the latency of neuronal responses and that of eye movements was studied in 59 neurons by changing the stimulus speed systematically (10-160 degrees/s). The latencies of both neuronal and ocular responses decreased as stimulus speed increased. As a result, the time difference between the response latencies for neuronal and ocular responses varied little with changes in stimulus speed. 4. Blurring of the random dot pattern, by interposing a sheet of ground glass, increased the latency of both neuronal responses and eye movements. 5. With the use of a check pattern instead of random dots, both neuronal and ocular responses began to decrease rapidly when the temporal frequency of the visual stimulus exceeded 20 Hz. At 40 Hz the neurons showed a distinctive burst-and-pause firing pattern, and the eye movements showed signs of oscillation. 6. The response properties of the MST neurons during ocular following were similar to those of the dorsolateral pontine nucleus (DLPN) neurons, reported previously. Our results indicate that the MST neurons may provide visual information to the DLPN neurons and may play a role in eliciting ocular following. 7. Responses during smooth-pursuit eye movement were studied in 55 MST neurons. Each of these neurons responded to the moving large-field visual stimulus, which elicited ocular following, and 40 of these neurons were activated during smooth pursuit in the dark. Response latencies during smooth pursuit were long in those neurons having different directional preferences during smooth pursuit and ocular following but were short for those having the same directional preferences during smooth pursuit and ocular following.(ABSTRACT TRUNCATED AT 400 WORDS)

Creativity has been proposed to be either the result of solely right hemisphere processes or of interhemispheric interactions. Little information is available, however, concerning the neuronal foundations of creativity. In this study, we introduced a new artistic task, designing a new tool (a pen), which let us quantitatively evaluate creativity by three indices of originality. These scores were analyzed in combination with brain activities measured by functional magnetic resonance imaging (fMRI). The results were compared between subjects who had been formally trained in design (experts) and novice subjects. In the experts, creativity was quantitatively correlated with the degree of dominance of the right prefrontal cortex over that of the left, but not with that of the right or left prefrontal cortex alone. In contrast, in novice subjects, only a negative correlation with creativity was observed in the bilateral inferior parietal cortex. We introduced structure equation modeling to analyze the interactions among these four brain areas and originality indices. The results predicted that training exerts a direct effect on the left parietal cortex. Additionally, as a result of the indirect effects, the activity of the right prefrontal cortex was facilitated, and the left prefrontal and right parietal cortices were suppressed. Our results supported the hypothesis that training increases creativity via reorganized intercortical interactions.