@article {80, title = {Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex}, journal = {Neuron}, volume = {49}, year = {2006}, month = {01/2006}, pages = {433 - 445}, abstract = {

Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized (LFPs) and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44\% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters.

}, issn = {08966273}, doi = {10.1016/j.neuron.2005.12.019}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627305011311}, author = {Kreiman, Gabriel and Hung, Chou P. and Kraskov, Alexander and Quiroga, Rodrigo Quian and Poggio, Tomaso and DiCarlo, James J.} } @article {88, title = {Fast Readout of Object Identity from Macaque Inferior Temporal Cortex}, journal = {Science}, volume = {310}, year = {2005}, month = {04/2005}, pages = {863 - 866}, abstract = {

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object \"identity\" and \"category.\" This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.

}, keywords = {Action Potentials, Animals, Brain Mapping, Macaca mulatta, Neurons, Psychology, Psychomotor Performance, Recognition, Temporal Lobe, Time Factors, Visual Perception}, issn = {0036-8075}, doi = {10.1126/science.1117593}, url = {https://www.sciencemag.org/lookup/doi/10.1126/science.1117593}, author = {Hung, Chou P. and Kreiman, Gabriel and Poggio, Tomaso and DiCarlo, James J.} } @article {93, title = {Ultra-fast object recognition from few spikes}, number = {2005-022}, year = {2005}, month = {07/2005}, pages = {1-31}, institution = {MIT}, address = {Cambridge, MA}, abstract = {

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 \Â\‘identity\Â\’ and \Â\‘category\Â\’, 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 \Â\‘categorization\Â\’ (a.k. basic level) and \Â\‘identification\Â\’ (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.

}, keywords = {AI, inferior temporal cortex, neural coding, object recognition}, issn = {2005-022}, url = {https://dspace.mit.edu/handle/1721.1/30556}, author = {Hung, Chou P. and Kreiman, Gabriel and Poggio, Tomaso and DiCarlo, James J.} } @proceedings {94, title = {Using {\textquoteleft}read-out{\textquoteright} of object identity to understand object coding in the macaque anterior inferior temporal cortex}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2005}, month = {03/2005}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

Recent efforts to develop robust computer vision systems capable of approaching the level of human object recognition performance have shown that models based on neurobiology can outperform non-biological models. To continue the development of such systems, we must understand the codes used by neuronal ensembles to represent object identity--a matter of continued debate in the neuroscience community. Understanding such codes would also allow the development of brain-machine interfaces capable of \‘reading-out\’ or \‘writing-in\’ information to AIT. One approach to understanding these neuronal codes is to obtain a large sample of neuronal data using a fixed set of objects, and then test the ability of various neuronal population measures (codes) to convey knowledge of object identity \– that is, determine the ability of each putative neuronal code to \‘read-out\’ object identity information from AIT. To this end, we have now recorded single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP), from over 500 sites in the anterior inferior temporal cortex (AIT) of two macaque monkeys while they viewed a fixed set of 77 complex objects. In this paper, we focus on the temporal latency and resolution of AIT neuronal population codes that best convey knowledge of object identity as assessed by object classification. Specifically, the 77 objects were divided before the experiment into eight different classes: toys, foodstuffs, human faces, monkey faces, hands, vehicles, boxes and cats/dogs. To evaluate the performance of each putative neuronal population code, we used support vector machines (SVM) and jackknife cross-validation to avoid over-fitting. Under the assumption of no covariance across spatially separate sites, these data and methods allowed us to examine putative neuronal population codes that differed in their latency (0 to 100 ms from stimulus onset) and their temporal resolution (12.5 to 200 ms wide bins). Results were evaluated for consistency across different sets of recording sites. We found that MUA and SUA spike count codes could perform single trial object classification. Performance improved with the number of sites and reached ~90\% when ~128 arbitrary AIT sites were included. For any given number of sites, increasing the temporal resolution beyond ~50 ms did not lead to improved performance and the optimal latency from stimulus onset was ~100 ms. Because the neuronal data were not collected simultaneously, we cannot rule out spatio-temporal codes with a time scale less than ~50 ms. However, these results support the hypothesis that the time scale of the object identity code in AIT is ~50 ms and that downstream read-out of object identity for behavior or further processing may simply integrate over this time scale.\ 

}, author = {Hung, Chou P. and Kreiman, Gabriel and Quiroga, Rodrigo Quian and Kraskov, Alexander and Poggio, Tomaso and DiCarlo, James J.} } @proceedings {96, title = {Object recognition by selective spike and LFP data in macaque inferior temporal cortex}, journal = {Society for Neuroscience}, year = {2004}, month = {10/2004}, publisher = {SFN}, address = {San Diego, CA, USA}, author = {Kreiman, Gabriel and Hung, Chou P. and Poggio, Tomaso and DiCarlo, James J.} } @article {98, title = {Selectivity of local field potentials in macaque inferior temporal cortex}, number = {2004-20}, year = {2004}, month = {09/2004}, institution = {MIT}, address = {Cambridge, M}, abstract = {

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 \&$\#$61549;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.

}, keywords = {AI, inferior temporal cortex, local field potentials, object recognition}, issn = {2004-020}, url = {https://dspace.mit.edu/handle/1721.1/30417}, author = {Kreiman, Gabriel and Hung, Chou P. and Poggio, Tomasso and DiCarlo, James J.} }