|Title||From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex|
|Publication Type||Conference Proceedings|
|Year of Publication||2011|
|Authors||Pagan, M, Neissi, AA, Baldassi, C, Zecchina, R, DiCarlo, JJ, Zoccolan, D|
|Conference Name||Computational and Systems Neuroscience (COSYNE)|
|Conference Location||Salt Lake City, Utah, USA|
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' 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.