@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 = {<p>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.</p>
},
	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}
}
@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 = {<p>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.</p>
},
	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}
}
