View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation
Name
nihms823581.pdf
Description
Accepted version
Size
1.03 MB
Format
Adobe PDF
Checksum (MD5)
b9dba6463e19680515bb2d3026b3d100
Author(s)
Leibo, Joel Z
Liao, Qianli
Anselmi, Fabio
Freiwald, Winrich A
Poggio, Tomaso
Date Issued
2017
Journal
Current Biology
Publisher
Elsevier BV
Citation
Leibo, J. Z., et al. "View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation." Curr Biol (2016).
Version
Author's final manuscript
Abstract
© 2017 Elsevier Ltd The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3–6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside.
MIT Department
Center for Brains, Minds, and Machines
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CUB.2016.10.015
