Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

Our mental representation of object categories is hierarchically organized, and our rapid and seemingly effortless categorization ability is crucial for our daily behavior. Here, we examine responses of a large number (>600) of neurons in monkey inferior temporal (IT) cortex with a large number (>1000) of natural and artificial object images. During the recordings the monkeys performed a passive fixation task. We found that the categorical structure of objects is represented by the pattern of activity distributed over the cell population. Animate and inanimate objects created distinguishable clusters in the population code. The global category of animate objects was divided into bodies, hands and faces. Faces were divided into primate and non-primate faces, and the primate-face group was divided into human and monkey faces. Bodies of human, birds, and four-limb animals clustered together, while lower animals such as fish, reptile and insects made another cluster. Thus, the cluster analysis showed that IT population responses reconstruct a large part of our intuitive category structure, including the global division into animate and inanimate objects, and further hierarchical subdivisions of animate objects. The representation of categories was distributed in several respects, e.g., the similarity of response patterns to stimuli within a category was maintained by both the cells that maximally responded to the category and the cells that responded weakly to the category. These results advance our understanding of the nature of the IT neural code, suggesting an inherently categorical representation that comprises a range of categories including the amply investigated face category.

We used Rapid Serial Visual Presentation (RSVP) to examine the tuning of macaque inferior temporal cortical (IT) neurons to 5 sets of 25 shapes each that varied systematically along pre-defined shape dimensions. A comparison of the RSVP technique using 100 ms presentations with that using a longer duration showed that shape preference can be determined with RSVP. Using relatively complex shapes that vary along relatively simple shape dimensions, we found that the large majority of neurons preferred extremes of the shape configuration, extending the results of a previous study using simpler shapes and a standard testing paradigm (Kayaert et al. 2005). A population analysis of the neuronal responses demonstrated that, in general, IT neurons can represent the similarities among the shapes at an ordinal level, extending a previous study that used a smaller number of shapes and a categorization task (Op de Beeck et al. 2001). However, the same analysis showed that IT neurons do not faithfully represent the physical similarities among the shapes. The responses to the two-part shapes could be predicted, virtually perfectly, from the average of the responses to the respective two parts presented in isolation. We also showed that IT neurons adapt to the stimulus distribution statistics. The neural shape discrimination improved when a shape set with a narrower stimulus range was presented, suggesting that the tuning of IT neurons is not static but adapts to the stimulus distribution statistics, at least when stimulated at a high rate with a restricted set of stimuli.

We investigated the organization of horizontal connections at two distinct hierarchical levels in the ventral visual cortical pathway of the monkey, the inferior temporal (TE) and primary visual (V1) cortices. After injections of anterograde tracers into layers 2 and 3, clusters of terminals ('patches') of labeled horizontal collaterals in TE appeared at various distances up to 8 mm from the injection site, while in V1 clear patches were distributed only within 2 mm. The size and spacing of these patches in TE were larger and more irregular than those observed in V1. The labeling intensity of patches in V1 declined sharply with distance from the injection site. This tendency was less obvious in TE; a number of densely labeled patches existed at distant sites beyond weakly labeled patches. While injections into both areas resulted in an elongated pattern of patches, the anisotropy was greater in TE than in V1 for injections of a similar size. Dual tracer injections and larger-sized injections further revealed that the adjacent sites in TE had spatially distinct horizontal projections, compared to those in V1. These area-specific characteristics of the horizontal connections may contribute to the differences in visual information processing of TE and V1.