Publication

Export 543 results:
In Press
Mlynarski, W. & Hermundstad, A. M. Adaptive Coding for Dynamic Sensory Inference. eLife (In Press).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Organization for Computational Neurosciences - CNS 2018 (In Press). at <http://www.cnsorg.org/cns-2018>
Palepu, A. & Kreiman, G. Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (In Press). at <https://embc.embs.org/2018/>
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. Proceedings of the Royal Society: Interface Focus (In Press).PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 MB)
Eric, W., Kevin, W. & Gabriel, K. Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (In Press). doi:10.1109/CISS.2018.8362305PDF icon 08362305.pdf (3.17 MB)
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (In Press).
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V. & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, (In Press).
2019
Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. International Conference on Learning Representations (2019).
Adler, A. & Wax, M. Blind Constant Modulus Multiuser Detection via Low-Rank Approximation. IEEE Signal Processing Letters 1 - 1 (2019). doi:10.1109/LSP.9710.1109/LSP.2019.2918001
Adler, A. & Wax, M. Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).
Lewis, O. & Hermann, K. Data for free: Fewer-shot algorithm learning with parametricity data augmentation. ICLR 2019 (2019).
Kell, A. J. E. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Current Opinion in Neurobiology 55, 121 - 132 (2019).
Adler, A., Araya-Polo, M. & Poggio, T. Deep Recurrent Architectures for Seismic Tomography. 81st EAGE Conference and Exhibition 2019 (2019).
Adler, A. & Wax, M. Direct Localization by Partly Calibrated Arrays: A Relaxed Maximum Likelihood Solution. 27th European Signal Processing Conference, EUSIPCO 2019 (2019). at <http://eusipco2019.org/technical-program>
Norman-Haignere, S. V., Kanwisher, N., McDermott, J. H. & Conway, B. R. Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones. Nature Neuroscience (2019). doi:10.1038/s41593-019-0410-7
Ullman, T. D. et al. Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. Cognitive Science Society (2019). at <https://mindmodeling.org/cogsci2019/papers/0506/index.html>PDF icon Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B. & DiCarlo, J. J. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019). doi:10.1038/s41593-019-0392-5PDF icon Author's last draft (1.74 MB)
Ponce, C. R. et al. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019).PDF icon Author's last draft (20.26 MB)
Araya-Polo, M., Adler, A., Farris, S. & Jennings, J. Deep Learning: Algorithms and Applications (SPRINGER-VERLAG, 2019).
Mhaskar, H. N. & Poggio, T. Function approximation by deep networks. (2019).PDF icon CBMM-Memo-098.pdf (687.36 KB)
Idiart, M. A. P., Villavicencio, A., Katz, B., Rennó-Costa, C. & Lisman, J. How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (2019).
Dobs, K., Isik, L., Pantazis, D. & Kanwisher, N. How face perception unfolds over time. Nature Communications 10, (2019).
Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs. Biological Psychiatry 85, 425 - 433 (2019).
Yildirim, I., Wu, J., Kanwisher, N. & Tenenbaum, J. B. An integrative computational architecture for object-driven cortex. Current Opinion in Neurobiology 55, 73 - 81 (2019).

Pages