Preprints, etc. are listed in Joelís CV
For information on citations to these papers, please refer to Google scholar
N.A. Cayco-Gajic and J. Zylberberg (2021). Good decisions require more than information. Nature Neuroscience. doi:10.1038/s41593-021-00883-9.
K. Ruda, J. Zylberberg, and G. Field (2020). Ignoring correlated activity causes a failure of retinal population codes. Nature Communications 11: 4605.
C. Federer, H. Xu, A. Fyshe, and J. Zylberberg (2020). Improved object recognition using neural networks trained to mimic the brainís statistical properties. Neural Networks 131: 103-114.
S. Jones, J. Zylberberg, and N. Schoppa (2020). Cellular and synaptic mechanisms that differentiate mitral cells and superficial tufted cells into parallel output channels in the olfactory bulb. Frontiers in Cellular Neuroscience 14: 443.
J. Cafaro, J. Zylberberg, and G. Field (2020). Global motion processing in populations of direction-selective retinal ganglion cells. Journal of Neuroscience 40: 5807-5819.
B.A. Richards, T. Lillicrap, et al., J. Zylberberg, D. Therien, K. Kording (2019). A deep learning framework for neuroscience. Nature Neuroscience 22: 1761.
J. A. Pruszynski, and J. Zylberberg (2019). The language of the brain: real-world neural population codes. Current Opinion in Neurobiology 58: 30.
W. Kindel, E. Christensen, and J. Zylberberg (2019). Using deep learning to probe the neural code for images in primary visual cortex. Journal of Vision 19: 29.
M. Lintz, J. Essig, J. Zylberberg, and G. Felsen (2019). Spatial representations in the superior colliculus are modulated by competition
among targets. Neuroscience 408: 191-203.
E. Christensen, A. Abosch, J. Thompson*, and J. Zylberberg* (2018). Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinsonís patients. Journal of Sleep Research e12806. (* co-senior authors who made equal contributions)
N.A. Cayco Gajic, J. Zylberberg, and E. Shea-Brown (2018). A moment-based maximum entropy model for fitting higher-order interactions in neural data. Entropy 20: 489.
C. Federer and J. Zylberberg (2018). A self-organizing short-term dynamical memory network. Neural Networks 106: 30-41.
J. Zylberberg and B. Strowbridge (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annual Review of Neuroscience 40: 603-627.
J. Zylberberg, A. Pouget, P. Latham, E. Shea-Brown (2017). Robust information propagation through noisy neural circuits. PLoS Computational Biology 13: e1005497.
J. Zylberberg*, J. Cafaro*, M.H. Turner*, E. Shea-Brown, and F. Rieke (2016). Direction-selective circuits shape noise to ensure a precise population code . Neuron 89: 369-383. (* co-first authors who made equal contributions)
J. Zylberberg, R.A. Hyde, and B.W. Strowbridge (2016). Dynamics of robust pattern separability in the hippocampal dentate gyrus. Hippocampus 29: 623-632.
J. Zylberberg and E. Shea-Brown (2015). Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations. Physical Review E 92: 062707.
N.A. Cayco Gajic, J. Zylberberg, and E. Shea-Brown (2015). Triplet correlations among similarly tuned cells impact population coding. Frontiers in Computational Neuroscience 9: 57.
Y. Hu, J. Zylberberg, and E. Shea-Brown (2014). The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes. PLoS Computational Biology 10: e1003469.
J. Zylberberg and M.R. DeWeese (2013). Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. PLoS Computational Biology 9: e1003182.
P. King, J. Zylberberg, and M.R. DeWeese (2013) Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1. Journal of Neuroscience 33: 5475.
J. Zylberberg, D. Pfau, and M.R. DeWeese (2012) Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments. Physical Review E 86: 066112.
J. Zylberberg, J.T. Murphy, and M.R. DeWeese (2011). A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. PLoS Computational Biology 7: e1002250.
J. Zylberberg and M.R. DeWeese (2011). How should prey animals respond to uncertain threats? Frontiers in Computational Neuroscience 5: 20.
G. Zhao, L. Pogosian, A. Silvestri, and J. Zylberberg (2009). Cosmological tests of general relativity with future tomographic surveys. Physical Review Letters 103: 241301.
G. Zhao, L. Pogosian, A. Silvestri, and J. Zylberberg (2009). Searching for modified growth patterns with tomographic surveys. Physical Review D 79: 083513.
C. Vockenhuber et al. (2008). Improvements of the DRAGON recoil separator at ISAC. Nuclear Instruments and Methods in Physics Research B 266: 4167.
J. Zylberberg et al. (2007). Charge-state distributions after radiative capture of helium nuclei by a carbon beam. Nuclear Instruments and Methods in Physics Research B 254: 17.
J. Zylberberg, A.A. Belik, E. Takayama-Muromachi, and Z.-G. Ye (2007). Bismuth Aluminate: A new high-TC lead-free piezo-/ferroelectric. Chemistry of Materials 19: 6385.
J. Bechhoefer, Y. Deng, J. Zylberberg, C. Lei, and Z.-G. Ye (2007). Temperature dependence of the capacitance of a ferroelectric material. American Journal of Physics 75: 1046.
J. Zylberberg and Z.-G. Ye (2006). Improved dielectric properties of bismuth-doped LaAlO3. Journal of Applied Physics 100: 086102.