No description available.

No description available.

While single neurons in inferior temporal (IT) cortex show differential responses to distinct complex stimuli, little is known about the responses of populations of neurons in IT. We recorded single electrode data, including multi-unit activity (MUA) and local field potentials (LFP), from 618 sites in the inferior temporal cortex of macaque monkeys while the animals passively viewed 78 different pictures of complex stimuli. The LFPs were obtained by low-pass filtering the extracellular electrophysiological signal with a corner frequency of 300 Hz. As reported previously, we observed that spike counts from MUA showed selectivity for some of the pictures. Strikingly, the LFP data, which is thought to constitute an average over large numbers of neurons, also showed significantly selective responses. The LFP responses were less selective than the MUA responses both in terms of the proportion of selective sites as well as in the selectivity of each site. We observed that there was only little overlap between the selectivity of MUA and LFP recordings from the same electrode. To assess the spatial organization of selective responses, we compared the selectivity of nearby sites recorded along the same penetration and sites recorded from different penetrations. We observed that MUA selectivity was correlated on spatial scales up to 800 m while the LFP selectivity was correlated over a larger spatial extent, with significant correlations between sites separated by several mm. Our data support the idea that there is some topographical arrangement to the organization of selectivity in inferior temporal cortex and that this organization may be relevant for the representation of object identity in IT.

Understanding the complex brain computations leading to object recognition requires quantitatively characterizing the information represented in inferior temporal cortex (IT), the highest stage of the primate visual stream. A read-out technique based on a trainable classifier is used to characterize the neural coding of selectivity and invariance at the population level. The activity of very small populations of independently recorded IT neurons (~100 randomly selected cells) over very short time intervals (as small as 12.5 ms) contains surprisingly accurate and robust information about both object ‘identity’ and ‘category’, which is furthermore highly invariant to object position and scale. Significantly, selectivity and invariance are present even for novel objects, indicating that these properties arise from the intrinsic circuitry and do not require object-specific learning. Within the limits of the technique, there is no detectable difference in the latency or temporal resolution of the IT information supporting so-called ‘categorization’ (a.k. basic level) and ‘identification’ (a.k. subordinate level) tasks. Furthermore, where information, in particular information about stimulus location and scale, can also be read-out from the same small population of IT neurons. These results show how it is possible to decode invariant object information rapidly, accurately and robustly from a small population in IT and provide insights into the nature of the neural code for different kinds of object-related information.