@article {15,
	title = {Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals.},
	journal = {eLife},
	volume = {7},
	year = {2018},
	month = {11/2018},
	abstract = {<p>Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably,~30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their responses to typical configurations of their preferred face parts relative to their response for atypical configurations even while neurons in higher areas achieved and maintained a preference for typical configurations. These hierarchical neural dynamics were inconsistent with standard feedforward circuits. Rather, recurrent models computing prediction errors between stages captured the observed temporal signatures. This model of neural dynamics, which simply augments the standard feedforward model of online vision, suggests that neural responses to static images may encode top-down prediction errors in addition to bottom-up feature estimates.</p>
},
	keywords = {Animals, Brain Mapping, Face, Humans, Macaca mulatta, Models, Neurological, Neurons, Pattern Recognition, Photic Stimulation, Reaction Time, Visual, Visual Cortex, Visual Perception},
	issn = {2050-084X},
	doi = {10.7554/eLife.42870},
	url = {https://elifesciences.org/articles/42870https://cdn.elifesciences.org/articles/42870/elife-42870-v2.pdf},
	author = {Issa, Elias B and Cadieu, Charles F and DiCarlo, James J}
}
@conference {37,
	title = {Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2013},
	month = {12/2013},
	address = {Lake Tahoe, Nevada, United States.},
	abstract = {<p>Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representational Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (features). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition.</p>
},
	doi = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream},
	url = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.pdf},
	author = {Daniel L. K. Yamins and Ha Hong and Cadieu, Charles F and DiCarlo, James J.}
}
