Journal
NEURON
Volume 96, Issue 1, Pages 89-+Publisher
CELL PRESS
DOI: 10.1016/j.neuron.2017.09.007
Keywords
-
Categories
Funding
- Human Frontier Science Program Long-Term Fellowship [LT001118/2012-L]
- Irma T. Hirschl/Monique Weill-Caulier Trusts
- Pew Scholar Award in the Biomedical Sciences
- McKnight Scholars Award
- New York Stem Cell Foundation
- National Eye Institute [R01 EY021594]
- National Center for Advancing Translational Sciences [CTSA UL1 TR001866]
- NSF Science and Technology Center for Brains, Minds, and Machines [CCF-1231216/5710003506]
- National Science Foundation [DBI-1343174]
- European Union's Horizon research and innovation programme under Marie Sklodowska-Curie [706519]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1343174] Funding Source: National Science Foundation
- Marie Curie Actions (MSCA) [706519] Funding Source: Marie Curie Actions (MSCA)
Ask authors/readers for more resources
Theories like predictive coding propose that lower-order brain areas compare their inputs to predictions derived from higher-order representations and signal their deviation as a prediction error. Here, we investigate whether the macaque face-processing system, a three-level hierarchy in the ventral stream, employs such a coding strategy. We show that after statistical learning of specific face sequences, the lower-level face area ML computes the deviation of actual from predicted stimuli. But these signals do not reflect the tuning characteristic of ML. Rather, they exhibit identity specificity and view invariance, the tuning properties of higher-level face areas AL and AM. Thus, learning appears to endow lower-level areas with the capability to test predictions at a higher level of abstraction than what is afforded by the feedforward sweep. These results provide evidence for computational architectures like predictive coding and suggest a new quality of functional organization of information-processing hierarchies beyond pure feedforward schemes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available