Performance-optimized hierarchical models predict neural responses in higher visual cortex
The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the hig...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 23; pp. 8619 - 8624 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
10.06.2014
National Acad Sciences |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization—applied in a biologically appropriate model class— can be used to build quantitative predictive models of neural processing. |
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| AbstractList | The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization--applied in a biologically appropriate model class--can be used to build quantitative predictive models of neural processing. Humans and monkeys easily recognize objects in scenes. This ability is known to be supported by a network of hierarchically interconnected brain areas. However, understanding neurons in higher levels of this hierarchy has long remained a major challenge in visual systems neuroscience. We use computational techniques to identify a neural network model that matches human performance on challenging object categorization tasks. Although not explicitly constrained to match neural data, this model turns out to be highly predictive of neural responses in both the V4 and inferior temporal cortex, the top two layers of the ventral visual hierarchy. In addition to yielding greatly improved models of visual cortex, these results suggest that a process of biological performance optimization directly shaped neural mechanisms. The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model’s categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model’s intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization—applied in a biologically appropriate model class—can be used to build quantitative predictive models of neural processing. Significance Humans and monkeys easily recognize objects in scenes. This ability is known to be supported by a network of hierarchically interconnected brain areas. However, understanding neurons in higher levels of this hierarchy has long remained a major challenge in visual systems neuroscience. We use computational techniques to identify a neural network model that matches human performance on challenging object categorization tasks. Although not explicitly constrained to match neural data, this model turns out to be highly predictive of neural responses in both the V4 and inferior temporal cortex, the top two layers of the ventral visual hierarchy. In addition to yielding greatly improved models of visual cortex, these results suggest that a process of biological performance optimization directly shaped neural mechanisms. The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model’s categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model’s intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization—applied in a biologically appropriate model class—can be used to build quantitative predictive models of neural processing. |
| Author | Hong, Ha Cadieu, Charles F. Seibert, Darren DiCarlo, James J. Yamins, Daniel L. K. Solomon, Ethan A. |
| Author_xml | – sequence: 1 givenname: Daniel L. K. surname: Yamins fullname: Yamins, Daniel L. K. – sequence: 2 givenname: Ha surname: Hong fullname: Hong, Ha – sequence: 3 givenname: Charles F. surname: Cadieu fullname: Cadieu, Charles F. – sequence: 4 givenname: Ethan A. surname: Solomon fullname: Solomon, Ethan A. – sequence: 5 givenname: Darren surname: Seibert fullname: Seibert, Darren – sequence: 6 givenname: James J. surname: DiCarlo fullname: DiCarlo, James J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24812127$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 1D.L.K.Y. and H.H. contributed equally to this work. Author contributions: D.L.K.Y., H.H., and J.J.D. designed research; D.L.K.Y., H.H., and E.A.S. performed research; D.L.K.Y. contributed new reagents/analytic tools; D.L.K.Y., H.H., C.F.C., and D.S. analyzed data; and D.L.K.Y., H.H., and J.J.D. wrote the paper. Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved April 8, 2014 (received for review March 3, 2014) |
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| References | 24876274 - Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8327-8 31341311 - Nature. 2019 Jul;571(7766):S15-S17 e_1_3_3_17_2 e_1_3_3_16_2 e_1_3_3_19_2 e_1_3_3_18_2 e_1_3_3_13_2 Krizhevsky A (e_1_3_3_36_2) 2012; 25 e_1_3_3_12_2 e_1_3_3_37_2 e_1_3_3_15_2 e_1_3_3_34_2 e_1_3_3_14_2 e_1_3_3_35_2 e_1_3_3_32_2 e_1_3_3_33_2 e_1_3_3_11_2 e_1_3_3_30_2 e_1_3_3_10_2 e_1_3_3_31_2 e_1_3_3_6_2 e_1_3_3_5_2 e_1_3_3_8_2 e_1_3_3_7_2 e_1_3_3_28_2 e_1_3_3_9_2 e_1_3_3_27_2 e_1_3_3_29_2 e_1_3_3_24_2 e_1_3_3_23_2 e_1_3_3_26_2 e_1_3_3_25_2 e_1_3_3_2_2 e_1_3_3_20_2 e_1_3_3_1_2 e_1_3_3_4_2 e_1_3_3_22_2 e_1_3_3_3_2 e_1_3_3_21_2 |
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| Snippet | The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains... Significance Humans and monkeys easily recognize objects in scenes. This ability is known to be supported by a network of hierarchically interconnected brain... Humans and monkeys easily recognize objects in scenes. This ability is known to be supported by a network of hierarchically interconnected brain areas.... |
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| SubjectTerms | Algorithms Animals Architectural models Architecture Biological Sciences Humans Information technology Macaca mulatta - physiology Modeling Models, Neurological Multilevel models Nerve Net - physiology Neural Networks, Computer Neurons Neurosciences Object recognition Parametric models Photic Stimulation - methods Physiology Predictive modeling Psychomotor Performance - physiology Recognition, Psychology - physiology Visual cortex Visual Cortex - physiology Visual Pathways - physiology Visual Perception - physiology |
| Title | Performance-optimized hierarchical models predict neural responses in higher visual cortex |
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