Trade-off between object selectivity and tolerance in monkey inferotemporal cortex

被引:124
作者
Zoccolan, Davide [1 ]
Kouh, Minjoon [1 ]
Poggio, Tomaso [1 ]
DiCarlo, James J. [1 ]
机构
[1] MIT, McGovern Inst Brain Res, Dept Brain & Cognit Sci, Ctr Biol & Computat Learning, Cambridge, MA 02139 USA
关键词
inferotemporal cortex; monkey; object recognition; multiple objects; trade-off; tolerance;
D O I
10.1523/JNEUROSCI.1897-07.2007
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Object recognition requires both selectivity among different objects and tolerance to vastly different retinal images of the same object, resulting from natural variation in (e.g.) position, size, illumination, and clutter. Thus, discovering neuronal responses that have object selectivity and tolerance to identity-preserving transformations is fundamental to understanding object recognition. Although selectivity and tolerance are found at the highest level of the primate ventral visual stream [ the inferotemporal cortex (IT)], both properties are highly varied and poorly understood. If an IT neuron has very sharp selectivity for a unique combination of object features ("diagnostic features"), this might automatically endow it with high tolerance. However, this relationship cannot be taken as given; although some IT neurons are highly object selective and some are highly tolerant, the empirical connection of these key properties is unknown. In this study, we systematically measured both object selectivity and tolerance to different identity-preserving image transformations in the spiking responses of a population of monkey IT neurons. We found that IT neurons with high object selectivity typically have low tolerance (and vice versa), regardless of how object selectivity was quantified and the type of tolerance examined. The discovery of this trade-off illuminates object selectivity and tolerance in IT and unifies a range of previous, seemingly disparate results. This finding also argues against the idea that diagnostic conjunctions of features guarantee tolerance. Instead, it is naturally explained by object recognition models in which object selectivity is built through AND-like tuning mechanisms.
引用
收藏
页码:12292 / 12307
页数:16
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