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Items: 32

1.

Learning to represent signals spike by spike.

Brendel W, Bourdoukan R, Vertechi P, Machens CK, Denève S.

PLoS Comput Biol. 2020 Mar 16;16(3):e1007692. doi: 10.1371/journal.pcbi.1007692. [Epub ahead of print]

2.

Decoding and encoding (de)mixed population responses.

Keemink SW, Machens CK.

Curr Opin Neurobiol. 2019 Oct;58:112-121. doi: 10.1016/j.conb.2019.09.004. Epub 2019 Sep 25. Review.

PMID:
31563083
3.

State-dependent geometry of population activity in rat auditory cortex.

Kobak D, Pardo-Vazquez JL, Valente M, Machens CK, Renart A.

Elife. 2019 Apr 10;8. pii: e44526. doi: 10.7554/eLife.44526.

4.

Cortical Areas Interact through a Communication Subspace.

Semedo JD, Zandvakili A, Machens CK, Yu BM, Kohn A.

Neuron. 2019 Apr 3;102(1):249-259.e4. doi: 10.1016/j.neuron.2019.01.026. Epub 2019 Feb 12.

5.

A living display system resolved pixel by pixel.

Jouary A, Machens CK.

Nature. 2018 Oct;562(7727):350-351. doi: 10.1038/d41586-018-05909-0. No abstract available.

PMID:
30333590
6.

Optimal compensation for neuron loss.

Barrett DG, Denève S, Machens CK.

Elife. 2016 Dec 9;5. pii: e12454. doi: 10.7554/eLife.12454.

7.

Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons.

Tian J, Huang R, Cohen JY, Osakada F, Kobak D, Machens CK, Callaway EM, Uchida N, Watabe-Uchida M.

Neuron. 2016 Sep 21;91(6):1374-1389. doi: 10.1016/j.neuron.2016.08.018. Epub 2016 Sep 8.

8.

Demixed principal component analysis of neural population data.

Kobak D, Brendel W, Constantinidis C, Feierstein CE, Kepecs A, Mainen ZF, Qi XL, Romo R, Uchida N, Machens CK.

Elife. 2016 Apr 12;5. pii: e10989. doi: 10.7554/eLife.10989.

9.

Efficient codes and balanced networks.

Denève S, Machens CK.

Nat Neurosci. 2016 Mar;19(3):375-82. doi: 10.1038/nn.4243. Review.

PMID:
26906504
10.

On the number of neurons and time scale of integration underlying the formation of percepts in the brain.

Wohrer A, Machens CK.

PLoS Comput Biol. 2015 Mar 20;11(3):e1004082. doi: 10.1371/journal.pcbi.1004082. eCollection 2015 Mar.

11.

Variability in neural activity and behavior.

Renart A, Machens CK.

Curr Opin Neurobiol. 2014 Apr;25:211-20. doi: 10.1016/j.conb.2014.02.013. Epub 2014 Mar 12. Review.

PMID:
24632334
12.

Optogenetic perturbations reveal the dynamics of an oculomotor integrator.

Gonçalves PJ, Arrenberg AB, Hablitzel B, Baier H, Machens CK.

Front Neural Circuits. 2014 Feb 28;8:10. doi: 10.3389/fncir.2014.00010. eCollection 2014.

13.

Predictive coding of dynamical variables in balanced spiking networks.

Boerlin M, Machens CK, Denève S.

PLoS Comput Biol. 2013;9(11):e1003258. doi: 10.1371/journal.pcbi.1003258. Epub 2013 Nov 14.

14.

Percept and the single neuron.

Wohrer A, Machens CK.

Nat Neurosci. 2013 Feb;16(2):112-3. doi: 10.1038/nn.3314. No abstract available.

PMID:
23354380
15.

Neuroscience. Building the human brain.

Machens CK.

Science. 2012 Nov 30;338(6111):1156-7. doi: 10.1126/science.1231865. No abstract available.

PMID:
23197519
16.

Population-wide distributions of neural activity during perceptual decision-making.

Wohrer A, Humphries MD, Machens CK.

Prog Neurobiol. 2013 Apr;103:156-93. doi: 10.1016/j.pneurobio.2012.09.004. Epub 2012 Nov 1. Review.

17.

Disentangling the functional consequences of the connectivity between optic-flow processing neurons.

Weber F, Machens CK, Borst A.

Nat Neurosci. 2012 Feb 12;15(3):441-8, S1-2. doi: 10.1038/nn.3044.

PMID:
22327473
18.

Demixing population activity in higher cortical areas.

Machens CK.

Front Comput Neurosci. 2010 Oct 6;4:126. doi: 10.3389/fncom.2010.00126. eCollection 2010.

19.

Spatiotemporal response properties of optic-flow processing neurons.

Weber F, Machens CK, Borst A.

Neuron. 2010 Aug 26;67(4):629-42. doi: 10.1016/j.neuron.2010.07.017.

20.

Functional, but not anatomical, separation of "what" and "when" in prefrontal cortex.

Machens CK, Romo R, Brody CD.

J Neurosci. 2010 Jan 6;30(1):350-60. doi: 10.1523/JNEUROSCI.3276-09.2010.

21.

Design of continuous attractor networks with monotonic tuning using a symmetry principle.

Machens CK, Brody CD.

Neural Comput. 2008 Feb;20(2):452-85.

PMID:
18047414
22.

From response to stimulus: adaptive sampling in sensory physiology.

Benda J, Gollisch T, Machens CK, Herz AV.

Curr Opin Neurobiol. 2007 Aug;17(4):430-6. Epub 2007 Aug 8. Review.

PMID:
17689952
23.

Modeling single-neuron dynamics and computations: a balance of detail and abstraction.

Herz AV, Gollisch T, Machens CK, Jaeger D.

Science. 2006 Oct 6;314(5796):80-5. Review.

PMID:
17023649
24.

Testing the efficiency of sensory coding with optimal stimulus ensembles.

Machens CK, Gollisch T, Kolesnikova O, Herz AV.

Neuron. 2005 Aug 4;47(3):447-56.

25.

Flexible control of mutual inhibition: a neural model of two-interval discrimination.

Machens CK, Romo R, Brody CD.

Science. 2005 Feb 18;307(5712):1121-4.

26.

Searching for optimal sensory signals: iterative stimulus reconstruction in closed-loop experiments.

Edin F, Machens CK, Schütze H, Herz AV.

J Comput Neurosci. 2004 Jul-Aug;17(1):47-56.

PMID:
15218353
27.

Linearity of cortical receptive fields measured with natural sounds.

Machens CK, Wehr MS, Zador AM.

J Neurosci. 2004 Feb 4;24(5):1089-100.

28.

Auditory modeling gets an edge.

Machens CK, Zador A.

J Neurophysiol. 2003 Dec;90(6):3581-2. No abstract available.

29.

Single auditory neurons rapidly discriminate conspecific communication signals.

Machens CK, Schütze H, Franz A, Kolesnikova O, Stemmler MB, Ronacher B, Herz AV.

Nat Neurosci. 2003 Apr;6(4):341-2. No abstract available.

PMID:
12652305
30.

Adaptive sampling by information maximization.

Machens CK.

Phys Rev Lett. 2002 Jun 3;88(22):228104. Epub 2002 May 20.

PMID:
12059456
31.

Energy-efficient coding with discrete stochastic events.

Schreiber S, Machens CK, Herz AV, Laughlin SB.

Neural Comput. 2002 Jun;14(6):1323-46.

PMID:
12020449
32.

Representation of acoustic communication signals by insect auditory receptor neurons.

Machens CK, Stemmler MB, Prinz P, Krahe R, Ronacher B, Herz AV.

J Neurosci. 2001 May 1;21(9):3215-27.

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