@article {35, title = {Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons}, journal = {PLoS Computational Biology}, volume = {9}, year = {2013}, month = {08/2013}, pages = {e1003167}, abstract = {

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex.

}, doi = {10.1371/journal.pcbi.1003167}, url = {https://dx.plos.org/10.1371/journal.pcbi.1003167}, author = {Baldassi, Carlo and Alemi-Neissi, Alireza and Pagan, Marino and DiCarlo, James J. and Zecchina, Riccardo and Zoccolan, Davide} } @article {41, title = {How Does the Brain Solve Visual Object Recognition?}, journal = {Neuron}, volume = {73}, year = {2012}, month = {01/2012}, pages = {415 - 434}, abstract = {

Mounting evidence suggests that \&$\#$39;core object recognition,\&$\#$39; the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from\ individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal.

}, issn = {08966273}, doi = {10.1016/j.neuron.2012.01.010}, url = {https://www.sciencedirect.com/science/article/pii/S089662731200092X}, author = {DiCarlo, ~J. and Zoccolan, Davide and Rust, ~C.} } @proceedings {44, title = {From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex}, journal = {Computational and Systems Neuroscience (COSYNE)}, year = {2011}, month = {02/2011}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

In primates, visual object information is processed through a hierarchy of cortico-cortical stages that culminates with the inferotemporal cortex (IT). Although the nature of visual processing in IT is still poorly understood, several lines of evidence suggest that IT conveys an explicit object representation that can directly serve as a basis for decision, action and memory - e.g., it can support flexible formation of semantic categories in downstream areas, such as prefrontal and perirhinal cortex. However, some recent studies (Kiani et al, 2007; Kriegeskorte et al, 2008) have argued that IT neuronal ensembles may themselves code the semantic membership of visual objects (i.e., represent such abstract conceptual classes such as animate and inanimate objects, animals, etc). In this study, we have applied an array of multi-variate computational approaches to investigate the nature of visual objects\&$\#$39; representation in IT. Our results show that IT neuronal ensembles represent a surprisingly broad spectrum of visual features complexity, ranging from low-level visual properties (e.g., brightness), to visual patterns of intermediate complexity (e.g., star-like shapes), to complex objects (e.g., four-leg animals) that appear to be coded so invariantly that their clustering in the IT neuronal space is not easily accountable by any similarity metric we used. On the one hand, these findings show that IT supports recognition of low-level properties of the visual input that are typically though to be extracted by lower-level visual areas. On the other hand, IT appears to convey such an explicit representation of some object classes that coding of semantic membership in IT (at least for a few categories) cannot be excluded. Overall, these results shed new light on IT amazing pluripotency in supporting recognition tasks as diverse as detection of brightness and categorization of complex shapes.

}, url = {http://www.cosyne.org/c/index.php?title=Cosyne_11_posters/I-89}, author = {Pagan, Marino and Neissi, AA and Baldassi, Carlo and Zecchina, Riccardo and DiCarlo, James J. and Zoccolan, Davide} } @article {53, title = {What Response Properties Do Individual Neurons Need to Underlie Position and Clutter {\textquotedblleft}Invariant{\textquotedblright} Object Recognition?}, journal = {Journal of Neurophysiology}, volume = {102}, year = {2009}, month = {01/2009}, pages = {360 - 376}, abstract = {

Primates can easily identify visual objects over large changes in retinal position\–a property commonly referred to as position \"invariance.\" This ability is widely assumed to depend on neurons in inferior temporal cortex {(IT)} that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most {IT} neurons\&$\#$39; responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of {IT} population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron\&$\#$39;s rank-order object preference under identity-preserving image transformations (e.g., clutter). Because {IT} neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream.

}, issn = {0022-3077}, doi = {10.1152/jn.90745.2008}, url = {https://www.physiology.org/doi/10.1152/jn.90745.2008}, author = {Li, Nuo and Cox, David D. and Zoccolan, Davide and DiCarlo, James J.} } @proceedings {73, title = {Is the rodent a valuable model system for studying invariant object recognition?}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

Despite the many advantages rodents offer in terms of experimental accessibility, they have never been extensively used as models to investigate the neuronal processing of visual objects. A crucial step in establishing whether rodents are suitable models for the study of object vision, is to assess if they are capable of invariant object recognition \– i.e., recognition of visual objects across the range of transformations that they typically undergo during natural vision (e.g., changes in position and size). In this study, we tested rats (Long-Evans) capability to discriminate between two geometrical shapes (square and triangle) presented at different sizes, positions and orientations. In each trial, a single shape was presented on a computer monitor and the animal had to report its identity. The experiment consisted of three phases. In phase I, each animal learned to discriminate between the two shapes presented at fixed size (40\° of visual angle), position (center of the monitor), and orientation. Na\ïve rats typically achieved \> 70\% correct performance in 2-3 weeks of training. In phase II, the animals were trained to perform the task while either the size, the horizontal position, or the orientation of the shapes was separately varied. Rats readily acquired this task (\> 70\% correct performance) across sizes ranging from 40\° to 10\°, positions spanning \±12\°, and orientations spanning \±40\°. During phase III of the experiment, we asked if rats could generalize to novel combinations of size, position, and orientation (a total of 100 transformations of each target shape were tested). Performance was typically \> 70\% correct for nearly all of these previously unseen transformations (performance of one rat over a subset of the tested conditions is shown in the figure) and even for a fraction of transformations for which feedback was withheld. These results show that rats can readily learn to: 1) discriminate between simple visual shapes; 2) disregard variations in their position, size and orientation; and 3) generalize to novel views. This suggests that the rat visual system contains the fundamental mechanisms that support object recognition. Therefore, given the broad ranges of experimental approaches available in rats, they may be a powerful new model system to study the neuronal basis of invariant object recognition

}, url = {http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf}, author = {Zoccolan, Davide and Cox, David D. and Oertelt, Nadja and Radwam, Basma and Tsang, Sabrina and DiCarlo, James J.} } @proceedings {85, title = {Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance}, journal = {Society for Neuroscience}, year = {2006}, month = {10/2006}, publisher = {SFN}, address = {Atlanta, GA, USA}, author = {Li, N. and Cox, David D. and Zoccolan, Davide and DiCarlo, James J.} } @proceedings {86, title = {Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex}, journal = {Gordon Conference: Sensation and the Natural Environment}, year = {2006}, month = {2006}, address = {Bozeman, MT, USA}, author = {Zoccolan, Davide and Kouh, M and Poggio, Tomaso and DiCarlo, James J.} } @proceedings {92, title = {Multiple object response normalization in monkey inferotemporal cortex}, journal = {Society for Neuroscience}, year = {2005}, month = {11/2005}, publisher = {SFN}, address = {Washington, DC, USA}, author = {Zoccolan, Davide and Cox, David D. and DiCarlo, James J.} }