@conference {5298,
	title = {Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units},
	booktitle = {NeurIPS},
	year = {2022},
	month = {10/2022},
	abstract = {<p>Humans are successfully able to recognize objects in a variety of image distributions. Today{\textquoteright}s artificial neural networks (ANNs), on the other hand, struggle to recognize objects in many image domains, especially those different from the training distribution. It is currently unclear which parts of the ANNs could be improved in order to close this generalization gap. In this work, we used recordings from primate high-level visual cortex (IT) to isolate whether ANNs lag behind primate generalization capabilities because of their encoder (transformations up to the penultimate layer), or their decoder (linear transformation into class labels). Specifically, we fit a linear decoder on images from one domain and evaluate transfer performance on twelve held-out domains, comparing fitting on primate IT representations vs. representations in ANN penultimate layers. To fairly compare, we scale the number of each ANN{\textquoteright}s units so that its in-domain performance matches that of the sampled IT population (i.e. 71 IT neural sites, 73\% binary-choice accuracy). We find that the sampled primate population achieves, on average, 68\% performance on the held-out-domains. Comparably sampled populations from ANN model units generalize less well, maintaining on average 60\%. This is independent of the number of sampled units: models{\textquoteright} out-of-domain accuracies consistently lag behind primate IT. These results suggest that making ANN model units more like primate IT will improve the generalization performance of ANNs.</p>
},
	url = {https://openreview.net/forum?id=iPF7mhoWkOl},
	author = {Ayu Marliawaty I Gusti Bagus and Tiago Marques and Sachi Sanghavi and James J. DiCarlo and Martin Schrimpf}
}
@article {4600,
	title = {The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys},
	journal = {Nature Communications},
	volume = {11},
	year = {2020},
	month = {08/2020},
	abstract = {<p>The ability to recognize written letter strings is foundational to human reading, but the underlying neuronal mechanisms remain largely unknown. Recent behavioral research in baboons suggests that non-human primates may provide an opportunity to investigate this question. We recorded the activity of hundreds of neurons in V4 and the inferior temporal cortex (IT) while na{\"\i}ve macaque monkeys passively viewed images of letters, English words and non-word strings, and tested the capacity of those neuronal representations to support a battery of orthographic processing tasks. We found that simple linear read-outs of IT (but not V4) population responses achieved high performance on all tested tasks, even matching the performance and error patterns of baboons on word classification. These results show that the IT cortex of untrained primates can serve as a precursor of orthographic processing, suggesting that the acquisition of reading in humans relies on the recycling of a brain network evolved for other visual functions.</p>
},
	doi = {10.1038/s41467-020-17714-3},
	url = {http://www.nature.com/articles/s41467-020-17714-3},
	author = {Rishi Rajalingham and Kohitij Kar and Sachi Sanghavi and Dehaene, Stanislas and James J. DiCarlo}
}
@conference {4528,
	title = {Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates },
	booktitle = {COSYNE},
	year = {2020},
	month = {02/2020},
	address = {Denver, CO, USA},
	author = {Martin Schrimpf and Fukushi Sato and Sachi Sanghavi and James J. DiCarlo}
}
