Stelios Papadopoulos on Muck Rack

Stelios Papadopoulos

London
As seen in: RTRA Intelligence, Nature, Seeking Alpha, Cell Press, BioCentury, SCI (Structured Credit Investor), PaySpace Magazine Global
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Articles

Author Correction: Foundation model of neural activity predicts response to new stimulus types

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By Eric Wang, Paul G. Fahey, Zhuokun Ding, Stelios Papadopoulos, Kayla Ponder, Marissa A Weis, Andersen Chang, Taliah Muhammad, Saumil S. Patel, Zhiwei Ding, Dat Q Tran, Jiakun Fu, Casey M. Schneider-Mizell, R. Clay Reid, Forrest Collman, Katrin Franke, Alexander S. Ecker, Jacob Reimer, Xaq Pitkow, Fabian Sinz, Andreas S. Tolias
| Nature Verified
Correction to: Nature https://doi.org/10.1038/s41586-025-08829-y Published online 9 April 2025 To ensure accurate documentation of the implemented models, we clarify several architectural details in the Methods describing the Conv-LSTM and CvT-LSTM architectures. These clarifications are limited to the Methods description and do not affect the results or conclusions.
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Statistics of natural scenes shape contextual modulation in the visual cortex

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By Jiakun Fu, Suhas Shrinivasan, Luca Baroni, Zhuokun Ding, Paul G. Fahey, Paweł A. Pierzchlewicz, Kayla Ponder, Rachel Froebe, Lydia Ntanavara, Taliah Muhammad, Konstantin F. Willeke, Eric Wang, Zhiwei Ding, Dat Tran, Stelios Papadopoulos, Saumil S. Patel, Jacob Reimer, Alexander S. Ecker, Xaq Pitkow, Ján Antolík, Fabian Sinz, Ralf M. Haefner, Andreas S. Tolias, Katrin Franke, Nikos Karantzas
| Cell Press Verified
Keywords contextual modulation primary visual cortex V1 natural image statistics center-surround interactions convolutional neural networks Bayesian inference visual perception mouse macaque Introduction Sensory processing is context dependent across species, enabling flexible behavior in changing environments.
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Functional bipartite invariance in mouse primary visual cortex receptive fields

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By Zhiwei Ding, Dat Tran, Kayla Ponder, Zhuokun Ding, Rachel Froebe, Lydia Ntanavara, Paul G. Fahey, Erick Cobos, Luca Baroni, Maria Diamantaki, Eric Wang, Andersen Chang, Stelios Papadopoulos, Jiakun Fu, Taliah Muhammad, Christos Papadopoulos, Santiago Cadena, Alexandros Evangelou, Konstantin F. Willeke, Fabio Anselmi, Sophia Sanborn, Ján Antolík, Emmanouil Froudarakis, Saumil S. Patel, Edgar Walker, Jacob Reimer, Fabian Sinz, Alexander S. Ecker, Katrin Franke, Xaq Pitkow, Andreas S. Tolias
| Nature Verified
Abstract Sensory systems support generalization by representing features that persist under input variation; however, identifying the neuronal basis of these invariances remains difficult due to high-dimensional and nonlinear neural computations. Here we leverage the inception loop paradigm, iterating between large-scale recordings, predictive models and in silico experiments with in vivo verification, to characterize neuronal invariances in mouse primary visual cortex (V1).
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