Publication
] A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences 46, 240 - 254 (2023).
Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).
CBMM-Memo-137.pdf (1.49 MB)
CBMM-Memo-137.pdf (1.49 MB) Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Human Brain Mapping (2022). doi:10.1002/hbm.25815
Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=Rpazl253IHb>
Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing. Current Biology (2019). doi:10.1016/j.cub.2019.08.020
Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019). 9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)
9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB) Uncovering representations of sleep-associated hippocampal ensemble spike activity. Scientific Reports 6, (2016).
Understanding "almost": Empirical and computational studies of near misses. 38th Annual Meeting of the Cognitive Science Society (2016). Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB)
Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB) Universal Dependencies for Learner English. (2016). memo-52_rev1.pdf (472.67 KB)
memo-52_rev1.pdf (472.67 KB) Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>
Unconscious perception of an opponent's goal. Vision Sciences Society Annual Meeting (VSS 2015) (2015). doi:10.1167/15.12.43
Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048