@article {28,
	title = {Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance},
	journal = {Journal of Neuroscience},
	volume = {35},
	year = {2015},
	month = {06/2017},
	pages = {13402 - 13418},
	abstract = {<p>To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (\&quot;face patches\&quot;) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of \&sim;60,000 IT neurons and is executed as a simple weighted sum of those firing rates.</p>
},
	issn = {0270-6474},
	doi = {10.1523/JNEUROSCI.5181-14.2015},
	url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.5181-14.2015},
	author = {Majaj, N. J. and Hong, H. and Solomon, E. A. and DiCarlo, J. J.}
}
@article {31,
	title = {Performance-optimized hierarchical models predict neural responses in higher visual cortex},
	journal = {Proceedings of the National Academy of Sciences},
	year = {2014},
	month = {10/2014},
	pages = {8619 - 8624},
	abstract = {<p>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\&rsquo;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\&rsquo;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\&mdash;applied in a biologically appropriate model class\&mdash;can be used to build quantitative predictive models of neural processing.</p>
},
	issn = {0027-8424},
	doi = {10.1073/pnas.1403112111},
	url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1403112111},
	author = {Yamins, D. L. K. and Hong, H. and Cadieu, C. F. and Solomon, E. A. and Seibert, D. and DiCarlo, J. J.}
}
