S. Idrees, F. Rieke, G.D. Field, and J. Zylberberg (2023). Biophysical neural adaptation mechanisms enable deep learning models to capture dynamic retinal computation. In revision for resubmission to Nature Communications. biorXiv: 2023.06.20.545728.

C. Gillon*, J. Pina*., et al., Y. Bengio, T. Lillicrap, B. Richards^, and J. Zylberberg^ (2023). Responses to pattern-violating visual stimuli evolve differently over days in somata and distal apical dendrites. Journal of Neuroscience, in press. (* co-first, and ^ co-senior authors who made equal contributions)

K. Carver, K. Saltoun, E. Christensen, J. Zylberberg*, A. Abosch*, and J. Thompson* (2023). Towards automated sleep-stage classification to enable adaptive deep brain stimulation targeting sleep in Parkinson’s disease. Communications Engineering 2: 95. (* co-senior authors who made equal contributions)

C. Efird, A. Murphy, J. Zylberberg, and A. Fyshe (2023). Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models. arXiv:2306.03375 [cs.AI].

D. Tang, J. Zylberberg, X. Jia, and H. Choi (2023). Stimulus-dependent functional network topology in mouse visual cortex. biorXiv: 2023.07.03.547364.

C. Gillon*, J. Lecoq*., et al., J. Zylberberg^, and B. Richards^ (2023). Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days. Scientific Data 10: 287. (* co-first, and ^ co-senior authors who made equal contributions)

R. Gerum, C. Pirlot, A. Fyshe, and J. Zylberberg (2022). Different spectral representations in optimized artificial neural networks and brains. arXiv:2208.10576 [cs.LG].

C. Pirlot, R. Gerum, C. Effird, J. Zylberberg, and A. Fyshe (2022). Improving the accuracy and robustness of CNNs using a deep CCA neural data regularizer. arXiv:2209.02582 [cs.CV].

N.A. Cayco-Gajic and J. Zylberberg (2021). Good decisions require more than information. Nature Neuroscience 24: 903-904.

C. Gillon, J. Pina, J. Lecoq, T. Henley, et al., Y. Bengio, T. Lillicrap, B. Richards, and J. Zylberberg (2021). Learning from unexpected events in the neocortical microcircuit. biorRxiv: 10.1101/2021.01.15.426915.

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 brain's 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.

E. Christensen and J. Zylberberg (2020). Models of the primate ventral stream that categorize and visualize images. bioRxiv: 10.1101/2020.02.21.958488.

B.A. Richards, T. Lillicrap, et al., J. Zylberberg, D. Therien, and 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.

N.A. Cayco Gajic, S. Durand, M. Buice, R. Iyer, C. Reid, J. Zylberberg, and E. Shea-Brown (2019). Transformation of population code from dLGN to V1 facilitates linear decoding. bioRxiv: 10.1101/826750.

E. Christensen, A. Abosch, J. Thompson*, and J. Zylberberg* (2018). Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinson's 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, and E. Shea-Brown (2017). Robust information propagation through noisy neural circuits. PLoS Computational Biology 13: e1005497.

J. Zylberberg (2017). The role of untuned neurons in sensory information coding. bioRxiv: 10.1101/134379.

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.