Maxwell Shinn
Affiliations: | Yale University, New Haven, CT |
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Parents
Sign in to add mentor| Edward Bullmore | grad student | 2015-2016 | Cambridge |
| Daeyeol Lee | grad student | 2016-2021 | Yale |
| John David Murray | grad student | 2016-2021 | Yale |
| Matteo Carandini | post-doc | 2021- | University College London (UCL) |
| Kenneth D. Harris | post-doc | 2021- | University College London (UCL) |
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Publications
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| Shinn M, Hu A, Turner L, et al. (2023) Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience |
| Bugeon S, Duffield J, Dipoppa M, et al. (2022) Publisher Correction: A transcriptomic axis predicts state modulation of cortical interneurons. Nature |
| Bugeon S, Duffield J, Dipoppa M, et al. (2022) A transcriptomic axis predicts state modulation of cortical interneurons. Nature. 607: 330-338 |
| Shinn M, Lee D, Murray JD, et al. (2022) Transient neuronal suppression for exploitation of new sensory evidence. Nature Communications. 13: 23 |
| Shinn M, Ehrlich D, Lee D, et al. (2020) Confluence of timing and reward biases in perceptual decision-making dynamics. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
| Shinn M, Lam NH, Murray JD. (2020) A flexible framework for simulating and fitting generalized drift-diffusion models. Elife. 9 |
| Burt JB, Helmer M, Shinn M, et al. (2020) Generative modeling of brain maps with spatial autocorrelation. Neuroimage. 117038 |
| Shinn M, Lam NH, Murray JD. (2020) Author response: A flexible framework for simulating and fitting generalized drift-diffusion models Elife |
| Seidlitz J, Váša F, Shinn M, et al. (2018) Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron. 97: 231-247.e7 |
| Romero-Garcia R, Whitaker KJ, Váša F, et al. (2017) Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. Neuroimage |