Publication

Found 906 results
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Meyers, E. A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
Meyers, E. M. NeuroDecodeR: a package for neural decoding in RData_Sheet_1.docx. Frontiers in Neuroinformatics 17, (2024).
Meyers, E. How PFC and LIP process single and multiple-object ‘pop-out’ displays. Society for Neuroscience (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Full%20Abstract%20PDFs%202015/SfN15_Abstracts_PDF_Nanos.ashx>
Meyers, E., Dean, M. & Hale, G. J. New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
Meyers, E. Dynamic population coding and its relationship to working memory. Journal of Neurophysiology 120, 2260 - 2268 (2018).
Meyers, E. Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).PDF icon Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)
Meyers, E., Borzello, M., Freiwald, W. A. & Tsao, D. Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System. The Journal of Neuroscience 35, (2015).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Mechanisms of Sensory Working Memory: Attention and Performance XXV. (Elsevier Inc. , 2015). at <https://www.sciencedirect.com/book/9780128013717/mechanisms-of-sensory-working-memory>
Mendoza-Halliday, D. et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nature Neuroscience 7, (2014).
Mendoza-Halliday, D. & Martinez-Trujillo, J. Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex. Nature Communications 8, (2017).
Mendoza-Halliday, D., Xu, H., Azevedo, F. A. C. & Desimone, R. Dissociable neuronal substrates of visual feature attention and working memory. Neuron 112, 850 - 863.e6 (2024).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)
Meanti*, G. et al. Estimating Koopman operators with sketching to provably learn large scale dynamical systems. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/f3d1e34a15c0af0954ae36a7f811c754-Paper-Conference.pdf>
McWalter, R. & McDermott, J. H. Illusory sound texture reveals multi-second statistical completion in auditory scene analysis. Nature Communications 10, (2019).
McPherson, M. J., Grace, R. C. & McDermott, J. H. Harmonicity aids hearing in noise. Attention, Perception, & Psychophysics (2022). doi:10.3758/s13414-021-02376-0
McPherson, M. J. & McDermott, J. H. Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory. Proceedings of the National Academy of Sciences 117, 32169 - 32180 (2020).
McNamee, D., Stachenfeld, K., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal-hippocampal system. Nature Neuroscience (2021). doi:10.1038/s41593-021-00831-7
McCoy, J. P. & Ullman, T. D. A Minimal Turing Test. Journal of Experimental Social Psychology 79, 1 - 8 (2018).
McCoy, J. P. & Ullman, T. Judgments of effort for magical violations of intuitive physics. PLOS ONE 14, e0217513 (2019).
Marques, T., Schrimpf, M. & DiCarlo, J. J. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv (2021).PDF icon 2021.03.01.433495v2.full_.pdf (3.23 MB)
Marques, T., Schrimpf, M. & DiCarlo, J. J. Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).

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