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Items: 1 to 20 of 99

1.

Searching for collective behavior in a large network of sensory neurons.

Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry MJ 2nd.

PLoS Comput Biol. 2014 Jan;10(1):e1003408. doi: 10.1371/journal.pcbi.1003408. Epub 2014 Jan 2.

2.

Thermodynamics and signatures of criticality in a network of neurons.

Tkačik G, Mora T, Marre O, Amodei D, Palmer SE, Berry MJ 2nd, Bialek W.

Proc Natl Acad Sci U S A. 2015 Sep 15;112(37):11508-13. doi: 10.1073/pnas.1514188112. Epub 2015 Sep 1.

3.

Stimulus-dependent maximum entropy models of neural population codes.

Granot-Atedgi E, Tkačik G, Segev R, Schneidman E.

PLoS Comput Biol. 2013;9(3):e1002922. doi: 10.1371/journal.pcbi.1002922. Epub 2013 Mar 14.

4.

Weak pairwise correlations imply strongly correlated network states in a neural population.

Schneidman E, Berry MJ 2nd, Segev R, Bialek W.

Nature. 2006 Apr 20;440(7087):1007-12. Epub 2006 Apr 9.

5.

The architecture of functional interaction networks in the retina.

Ganmor E, Segev R, Schneidman E.

J Neurosci. 2011 Feb 23;31(8):3044-54. doi: 10.1523/JNEUROSCI.3682-10.2011.

6.

Exact computation of the maximum-entropy potential of spiking neural-network models.

Cofré R, Cessac B.

Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052117. Epub 2014 May 12.

PMID:
25353749
7.

Optimal population coding by noisy spiking neurons.

Tkacik G, Prentice JS, Balasubramanian V, Schneidman E.

Proc Natl Acad Sci U S A. 2010 Aug 10;107(32):14419-24. doi: 10.1073/pnas.1004906107. Epub 2010 Jul 26.

8.

The structured 'low temperature' phase of the retinal population code.

Ioffe ML, Berry MJ 2nd.

PLoS Comput Biol. 2017 Oct 11;13(10):e1005792. doi: 10.1371/journal.pcbi.1005792. eCollection 2017 Oct.

9.

A thesaurus for a neural population code.

Ganmor E, Segev R, Schneidman E.

Elife. 2015 Sep 8;4. doi: 10.7554/eLife.06134.

10.

Prediction of spatiotemporal patterns of neural activity from pairwise correlations.

Marre O, El Boustani S, Frégnac Y, Destexhe A.

Phys Rev Lett. 2009 Apr 3;102(13):138101. Epub 2009 Apr 2.

PMID:
19392405
11.

A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro.

Tang A, Jackson D, Hobbs J, Chen W, Smith JL, Patel H, Prieto A, Petrusca D, Grivich MI, Sher A, Hottowy P, Dabrowski W, Litke AM, Beggs JM.

J Neurosci. 2008 Jan 9;28(2):505-18. doi: 10.1523/JNEUROSCI.3359-07.2008.

12.

Computing with neural synchrony.

Brette R.

PLoS Comput Biol. 2012;8(6):e1002561. doi: 10.1371/journal.pcbi.1002561. Epub 2012 Jun 14.

13.

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

Shimazaki H, Amari S, Brown EN, Grün S.

PLoS Comput Biol. 2012;8(3):e1002385. doi: 10.1371/journal.pcbi.1002385. Epub 2012 Mar 8.

14.

Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.

Ito S, Hansen ME, Heiland R, Lumsdaine A, Litke AM, Beggs JM.

PLoS One. 2011;6(11):e27431. doi: 10.1371/journal.pone.0027431. Epub 2011 Nov 15.

15.

Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.

Legenstein R, Maass W.

PLoS Comput Biol. 2014 Oct 23;10(10):e1003859. doi: 10.1371/journal.pcbi.1003859. eCollection 2014 Oct.

16.

Noise-Robust Modes of the Retinal Population Code Have the Geometry of "Ridges" and Correspond to Neuronal Communities.

Loback A, Prentice J, Ioffe M, Berry Ii M.

Neural Comput. 2017 Dec;29(12):3119-3180. doi: 10.1162/neco_a_01011. Epub 2017 Sep 28.

PMID:
28957022
17.

Maximally informative pairwise interactions in networks.

Fitzgerald JD, Sharpee TO.

Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Sep;80(3 Pt 1):031914. Epub 2009 Sep 23.

18.

A small world of neuronal synchrony.

Yu S, Huang D, Singer W, Nikolic D.

Cereb Cortex. 2008 Dec;18(12):2891-901. doi: 10.1093/cercor/bhn047. Epub 2008 Apr 9.

19.

Computing complex visual features with retinal spike times.

Gütig R, Gollisch T, Sompolinsky H, Meister M.

PLoS One. 2013;8(1):e53063. doi: 10.1371/journal.pone.0053063. Epub 2013 Jan 2.

20.

Probabilistic models for neural populations that naturally capture global coupling and criticality.

Humplik J, Tkačik G.

PLoS Comput Biol. 2017 Sep 19;13(9):e1005763. doi: 10.1371/journal.pcbi.1005763. eCollection 2017 Sep.

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