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Items: 15

1.

Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinson's patients.

Christensen E, Abosch A, Thompson JA, Zylberberg J.

J Sleep Res. 2018 Dec 13:e12806. doi: 10.1111/jsr.12806. [Epub ahead of print]

PMID:
30549130
2.

A self-organizing short-term dynamical memory network.

Federer C, Zylberberg J.

Neural Netw. 2018 Oct;106:30-41. doi: 10.1016/j.neunet.2018.06.008. Epub 2018 Jun 20.

3.

Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory.

Zylberberg J, Strowbridge BW.

Annu Rev Neurosci. 2017 Jul 25;40:603-627. doi: 10.1146/annurev-neuro-070815-014006. Review.

4.

Robust information propagation through noisy neural circuits.

Zylberberg J, Pouget A, Latham PE, Shea-Brown E.

PLoS Comput Biol. 2017 Apr 18;13(4):e1005497. doi: 10.1371/journal.pcbi.1005497. eCollection 2017 Apr.

5.

Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code.

Zylberberg J, Cafaro J, Turner MH, Shea-Brown E, Rieke F.

Neuron. 2016 Jan 20;89(2):369-383. doi: 10.1016/j.neuron.2015.11.019.

6.

Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations.

Zylberberg J, Shea-Brown E.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062707. doi: 10.1103/PhysRevE.92.062707. Epub 2015 Dec 9.

PMID:
26764727
7.

Dynamics of robust pattern separability in the hippocampal dentate gyrus.

Zylberberg J, Hyde RA, Strowbridge BW.

Hippocampus. 2016 May;26(5):623-32. doi: 10.1002/hipo.22546. Epub 2015 Nov 5.

8.

Triplet correlations among similarly tuned cells impact population coding.

Cayco-Gajic NA, Zylberberg J, Shea-Brown E.

Front Comput Neurosci. 2015 May 18;9:57. doi: 10.3389/fncom.2015.00057. eCollection 2015.

9.

The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes.

Hu Y, Zylberberg J, Shea-Brown E.

PLoS Comput Biol. 2014 Feb 27;10(2):e1003469. doi: 10.1371/journal.pcbi.1003469. eCollection 2014 Feb.

10.

Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.

Zylberberg J, DeWeese MR.

PLoS Comput Biol. 2013;9(8):e1003182. doi: 10.1371/journal.pcbi.1003182. Epub 2013 Aug 29.

11.

Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.

King PD, Zylberberg J, DeWeese MR.

J Neurosci. 2013 Mar 27;33(13):5475-85. doi: 10.1523/JNEUROSCI.4188-12.2013.

12.

Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments.

Zylberberg J, Pfau D, Deweese MR.

Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Dec;86(6 Pt 2):066112. Epub 2012 Dec 14.

PMID:
23368009
13.

A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.

Zylberberg J, Murphy JT, DeWeese MR.

PLoS Comput Biol. 2011 Oct;7(10):e1002250. doi: 10.1371/journal.pcbi.1002250. Epub 2011 Oct 27.

14.

How should prey animals respond to uncertain threats?

Zylberberg J, Deweese MR.

Front Comput Neurosci. 2011 Apr 25;5:20. doi: 10.3389/fncom.2011.00020. eCollection 2011.

15.

Cosmological tests of general relativity with future tomographic surveys.

Zhao GB, Pogosian L, Silvestri A, Zylberberg J.

Phys Rev Lett. 2009 Dec 11;103(24):241301. Epub 2009 Dec 8.

PMID:
20366194

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