2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-2B.15
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Selective behavioral deficits from focal inactivation of primate inferior temporal (IT) cortex: a new quantitative constraint for models of core object recognition
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1056-0
Authors: Rishi Rajalingham, Hyodong Lee, James J. DiCarlo, Massachusetts Institute of Technology, United States
Abstract: Primate core visual object recognition is thought to rely on the ventral visual stream, a hierarchy of cortical areas culminating in inferior temporal (IT) cortex. Previous work has shown that the IT population responses accurately predict primate object recognition behavior, suggesting that these IT codes underlie these behaviors. However, direct causal evidence for this decoding hypothesis has been equivocal at best, especially beyond the specific case of face-selective sub-regions of IT. Here, we tested the general causal role of IT in core object recognition by reversibly inactivating individual, millimeter-scale regions of IT via injection of muscimol while monkeys performed several binary object discrimination tasks, interleaved trial-by-trial. Our results show that inactivating different millimeter-scale sub-regions of primate IT resulted in different patterns of task deficits. These results provide new constraints for computational models of the ventral stream and this behavior. To this end, we tested state-of-the-art deep convolutional neural network models by constructing “topographic deep artificial neural networks (TDANNs) on which we simulated inactivation experiments. Our results show that TDANNs recapitulated first-order experimental phenomena slightly better than randomly mapped deep artificial neural network models. Taken together, these results establish and test a new class of experimental constraints for computational models of core object recognition.