J Swaroop Guntupalli
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Explore the profile of J Swaroop Guntupalli including associated specialties, affiliations and a list of published articles.
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26
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1449
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Recent Articles
1.
Raju R, Guntupalli J, Zhou G, Wendelken C, Lazaro-Gredilla M, George D
Sci Adv
. 2024 Jul;
10(31):eadm8470.
PMID: 39083616
Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here,...
2.
George D, Rikhye R, Gothoskar N, Guntupalli J, Dedieu A, Lazaro-Gredilla M
Nat Commun
. 2021 Apr;
12(1):2392.
PMID: 33888694
Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to...
3.
Busch E, Slipski L, Feilong M, Guntupalli J, Visconti di Oleggio Castello M, Huckins J, et al.
Neuroimage
. 2021 Mar;
233:117975.
PMID: 33762217
Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals' brain data into a common model space while...
4.
Feilong M, Guntupalli J, Haxby J
Elife
. 2021 Mar;
10.
PMID: 33683205
Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous...
5.
George D, Lazaro-Gredilla M, Guntupalli J
Front Comput Neurosci
. 2020 Nov;
14:554097.
PMID: 33192426
Despite the recent progress in AI powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and...
6.
Lazaro-Gredilla M, Lin D, Guntupalli J, George D
Sci Robot
. 2020 Nov;
4(26).
PMID: 33137758
Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent...
7.
Haxby J, Guntupalli J, Nastase S, Feilong M
Elife
. 2020 Jun;
9.
PMID: 32484439
Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment captures shared information by projecting pattern vectors for neural responses and connectivities into a common, high-dimensional...
8.
Jiahui G, Feilong M, Visconti di Oleggio Castello M, Guntupalli J, Chauhan V, Haxby J, et al.
Neuroimage
. 2019 Dec;
216:116458.
PMID: 31843709
Subject-specific, functionally defined areas are conventionally estimated with functional localizers and a simple contrast analysis between responses to different stimulus categories. Compared with functional localizers, naturalistic stimuli provide several advantages...
9.
Feilong M, Nastase S, Guntupalli J, Haxby J
Neuroimage
. 2018 Aug;
183:375-386.
PMID: 30118870
Fine-grained functional organization of cortex is not well-conserved across individuals. As a result, individual differences in cortical functional architecture are confounded by topographic idiosyncrasies-i.e., differences in functional-anatomical correspondence. In this...
10.
Guntupalli J, Feilong M, Haxby J
PLoS Comput Biol
. 2018 Apr;
14(4):e1006120.
PMID: 29664910
Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to...