@article {177,
	title = {A Unifying Principle for the Functional Organization of Visual Cortex},
	journal = {bioRxiv},
	year = {2023},
	month = {2023},
	abstract = {<p>A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles\&nbsp;underlying the emergence and utility of functional organization are poorly understood. Here we develop\&nbsp;the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the\&nbsp;functional organization of multiple cortical areas in the primate visual system. We analyze the key factorsresponsible for the TDANN\&rsquo;s success and find that it strikes a balance between two specific objectives:achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of\&nbsp;responses across the cortical sheet according to a metric that scales relative to cortical surface area. In\&nbsp;turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in\&nbsp;models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN\&rsquo;s functionalorganization balances performance with inter-area connection length, and use the resulting models for\&nbsp;a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle\&nbsp;for understanding functional organization and a novel view of the functional role of the visual system in\&nbsp;particular.</p>
},
	doi = { https://doi.org/10.1101/2023.05.18.541361},
	url = {https://www.biorxiv.org/content/10.1101/2023.05.18.541361v1.full.pdf},
	author = {Margalit, Eshed and Lee, Hyodong and Finzi, Dawn and DiCarlo, James J and Grill-Spector, Kalanit and Yamins, Daniel LK}
}
