jZ Lab Publications

Preprints, etc. are listed in Joelís CV.
For information on citations to these papers, please refer to Google scholar.

E. Christensen, A. Abosch, J. Thompson*, and J. Zylberberg*. Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinsonís patients . To appear in the Journal of Sleep Research. (* 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.