@article {5535,
	title = {Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract},
	journal = {Nature Communications},
	volume = {14},
	year = {2023},
	month = {03/2023},
	abstract = {<p>Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities {\textendash} inherited from over 500 million years of evolution {\textendash} that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.</p>
},
	doi = {10.1038/s41467-023-37180-x},
	url = {https://www.nature.com/articles/s41467-023-37180-x},
	author = {Zador, Anthony and Escola, Sean and Richards, Blake and {\"O}lveczky, Bence and Bengio, Yoshua and Boahen, Kwabena and Botvinick, Matthew and Chklovskii, Dmitri and Churchland, Anne and Clopath, Claudia and DiCarlo, James and Ganguli, Surya and Hawkins, Jeff and K{\"o}rding, Konrad and Koulakov, Alexei and LeCun, Yann and Lillicrap, Timothy and Marblestone, Adam and Olshausen, Bruno and Pouget, Alexandre and Savin, Cristina and Sejnowski, Terrence and Simoncelli, Eero and Solla, Sara and Sussillo, David and Tolias, Andreas S. and Tsao, Doris}
}
@article {5539,
	title = {Emergence of Sparse Representations from Noise},
	year = {2023},
	month = {07/2023},
	address = {Honolulu, HI},
	abstract = {<p>A hallmark of biological neural networks, which distinguishes them from their artificial counter- parts, is the high degree of sparsity in their acti- vations. This discrepancy raises three questions our work helps to answer: (i) Why are biologi- cal networks so sparse? (ii) What are the bene- fits of this sparsity? (iii) How can these benefits be utilized by deep learning models? Our an- swers to all of these questions center around train- ing networks to handle random noise. Surpris- ingly, we discover that noisy training introduces three implicit loss terms that result in sparsely firing neurons specializing to high variance fea- tures of the dataset. When trained to reconstruct noisy-CIFAR10, neurons learn biological recep- tive fields. More broadly, noisy training presents a new approach to potentially increase model inter- pretability with additional benefits to robustness and computational efficiency.</p>
},
	url = {https://openreview.net/pdf?id=cxYaBAXVKg},
	author = {Trenton Bricken and Rylan Schaeffer and Olshausen, Bruno and Gabriel Kreiman}
}
