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Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning.
A quantitative description of membrane current and its application to conduction and excitation in nerve
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A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution
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Fourier neural operator for parametric partial differential equations
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Functional analysis, Sobolev spaces and partial differential equations , volume 2
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How advances in neural recording affect data analysis
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Ultrasensitive fluorescent proteins for imaging neuronal activity
Chen, T.-W., Wardill, T. J., Sun, Y., Pulver, S. R., Renninger, S. L., Baohan, A., Schreiter, E. R., Kerr, R. A., Orger, M. B., Jayaraman, V., et al · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J · 2014
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Delving deeper into convolutional networks for learning video representations
Ballas, N., Yao, L., Pal, C., and Courville, A · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c · 2015
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Deep predictive coding networks for video prediction and unsupervised learning
Lotter, W., Kreiman, G., and Cox, D · 2016
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Brain structure and dynamics across scales: in search of rules
Wang, X.-J. and Kennedy, H · 2016
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Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. and DiCarlo, J. J · 2016
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Sobolev training for neural networks
Czarnecki, W. M., Osindero, S., Jaderberg, M., Swirszcz, G., and Pascanu, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Cortical travelling waves: mechanisms and computational principles
Muller, L., Chavane, F., Reynolds, J., and Sejnowski, T. J · 2018
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Inferring single-trial neural population dynamics using sequential auto-encoders
Pandarinath, C., O’Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., et al · 2018
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Computation through neural population dynamics
Vyas, S., Golub, M. D., Sussillo, D., and Shenoy, K. V · 2020
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Integrating physics-based modeling with machine learning: A survey
Willard, J., Jia, X., Xu, S., Steinbach, M., and Kumar, V · 2020
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Vivit: A video vision transformer
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., and Schmid, C · 2021
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A flow-based latent state generative model of neural population responses to natural images
Bashiri, M., Walker, E., Lurz, K.-K., Jagadish, A., Muhammad, T., Ding, Z., Ding, Z., Tolias, A., and Sinz, F · 2021
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Is space-time attention all you need for video understanding?
Bertasius, G., Wang, H., and Torresani, L · 2021
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Choose a transformer: Fourier or galerkin
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Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
Sinz, F., Ecker, A. S., Fahey, P., Walker, E., Cobos, E., Froudarakis, E., Yatsenko, D., Pitkow, Z., Reimer, J., and Tolias, A · 2018
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Recurrent computations for visual pattern completion
Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Ortega Caro, J., Hardesty, W., Cox, D., and Kreiman, G · 2018
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Deep convolutional models improve predictions of macaque v1 responses to natural images
Cadena, S. A., Denfield, G. H., Walker, E. Y., Gatys, L. A., Tolias, A. S., Bethge, M., and Ecker, A. S · 2019
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Latent ordinary differential equations for irregularly-sampled time series
Rubanova, Y., Chen, R. T., and Duvenaud, D. K · 2019
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Inception loops discover what excites neurons most using deep predictive models
Walker, E. Y., Sinz, F. H., Cobos, E., Muhammad, T., Froudarakis, E., Fahey, P. G., Ecker, A. S., Reimer, J., Pitkow, X., and Tolias, A. S · 2019
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Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits
Barson, D., Hamodi, A. S., Shen, X., Lur, G., Constable, R. T., Cardin, J. A., Crair, M. C., and Higley, M. J · 2020
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Mesoscopic imaging: shining a wide light on large-scale neural dynamics
Cardin, J. A., Crair, M. C., and Higley, M. J · 2020
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Cao, S · 2021
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Inferring latent dynamics underlying neural population activity via neural differential equations
Kim, T. D., Luo, T. Z., Pillow, J. W., and Brody, C. D · 2021
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Neural operator: Learning maps between function spaces
Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2021
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Sobolev training for the neural network solutions of pdes
Son, H., Jang, J. W., Han, W. J., and Hwang, H. J · 2021
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Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
Vlassis, N. N. and Sun, W · 2021
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Nyströmformer: A nystöm-based algorithm for approximating self-attention
Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., and Singh, V · 2021
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How to understand masked autoencoders
Cao, S., Xu, P., and Clifton, D. A · 2022
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Cardona, J. E. S. and Hecht, M · 2022
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Data-driven prediction in dynamical systems: recent developments
Ghadami, A. and Epureanu, B. I · 2022
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Learning continuous models for continuous physics
Krishnapriyan, A. S., Queiruga, A. F., Erichson, N. B., and Mahoney, M. W · 2022
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Robust deep learning object recognition models rely on low frequency information in natural images
Li, Z., Caro, J. O., Rusak, E., Brendel, W., Bethge, M., Anselmi, F., Patel, A. B., Tolias, A. S., and Pitkow, X · 2022
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Zappala, E., Fonseca, A. H. d. O., Caro, J. O., and van Dijk, D · 2022
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