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Items: 30

1.

Embodied Synaptic Plasticity With Online Reinforcement Learning.

Kaiser J, Hoff M, Konle A, Vasquez Tieck JC, Kappel D, Reichard D, Subramoney A, Legenstein R, Roennau A, Maass W, Dillmann R.

Front Neurorobot. 2019 Oct 3;13:81. doi: 10.3389/fnbot.2019.00081. eCollection 2019.

2.

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Pokorny C, Ison MJ, Rao A, Legenstein R, Papadimitriou C, Maass W.

Cereb Cortex. 2019 Aug 12. pii: bhz140. doi: 10.1093/cercor/bhz140. [Epub ahead of print]

PMID:
31403679
3.

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

Yan Y, Kappel D, Neumarker F, Partzsch J, Vogginger B, Hoppner S, Furber S, Maass W, Legenstein R, Mayr C.

IEEE Trans Biomed Circuits Syst. 2019 Jun;13(3):579-591. doi: 10.1109/TBCAS.2019.2906401. Epub 2019 Mar 27.

PMID:
30932847
4.

Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype.

Liu C, Bellec G, Vogginger B, Kappel D, Partzsch J, Neumärker F, Höppner S, Maass W, Furber SB, Legenstein R, Mayr CG.

Front Neurosci. 2018 Nov 16;12:840. doi: 10.3389/fnins.2018.00840. eCollection 2018.

5.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

Kappel D, Legenstein R, Habenschuss S, Hsieh M, Maass W.

eNeuro. 2018 Apr 24;5(2). pii: ENEURO.0301-17.2018. doi: 10.1523/ENEURO.0301-17.2018. eCollection 2018 Mar-Apr.

6.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

Jonke Z, Legenstein R, Habenschuss S, Maass W.

J Neurosci. 2017 Aug 30;37(35):8511-8523. doi: 10.1523/JNEUROSCI.2078-16.2017. Epub 2017 Jul 31.

7.

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.

Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T.

Nat Commun. 2016 Sep 29;7:12611. doi: 10.1038/ncomms12611.

8.

Network Plasticity as Bayesian Inference.

Kappel D, Habenschuss S, Legenstein R, Maass W.

PLoS Comput Biol. 2015 Nov 6;11(11):e1004485. doi: 10.1371/journal.pcbi.1004485. eCollection 2015 Nov.

9.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.

Bill J, Buesing L, Habenschuss S, Nessler B, Maass W, Legenstein R.

PLoS One. 2015 Aug 18;10(8):e0134356. doi: 10.1371/journal.pone.0134356. eCollection 2015.

10.

Computer science: Nanoscale connections for brain-like circuits.

Legenstein R.

Nature. 2015 May 7;521(7550):37-8. doi: 10.1038/521037a. No abstract available.

PMID:
25951279
11.

A compound memristive synapse model for statistical learning through STDP in spiking neural networks.

Bill J, Legenstein R.

Front Neurosci. 2014 Dec 16;8:412. doi: 10.3389/fnins.2014.00412. eCollection 2014.

12.

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.

13.

A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling.

Blackman AV, Grabuschnig S, Legenstein R, Sjöström PJ.

Front Neuroanat. 2014 Jul 11;8:65. doi: 10.3389/fnana.2014.00065. eCollection 2014.

14.

Integration of nanoscale memristor synapses in neuromorphic computing architectures.

Indiveri G, Linares-Barranco B, Legenstein R, Deligeorgis G, Prodromakis T.

Nanotechnology. 2013 Sep 27;24(38):384010. doi: 10.1088/0957-4484/24/38/384010. Epub 2013 Sep 2.

PMID:
23999381
15.

Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning.

Hoerzer GM, Legenstein R, Maass W.

Cereb Cortex. 2014 Mar;24(3):677-90. doi: 10.1093/cercor/bhs348. Epub 2012 Nov 11.

PMID:
23146969
16.

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons.

Legenstein R, Maass W.

J Neurosci. 2011 Jul 27;31(30):10787-802. doi: 10.1523/JNEUROSCI.5684-10.2011.

17.

Reinforcement learning on slow features of high-dimensional input streams.

Legenstein R, Wilbert N, Wiskott L.

PLoS Comput Biol. 2010 Aug 19;6(8). pii: e1000894. doi: 10.1371/journal.pcbi.1000894.

18.

A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.

Legenstein R, Chase SM, Schwartz AB, Maass W.

J Neurosci. 2010 Jun 23;30(25):8400-10. doi: 10.1523/JNEUROSCI.4284-09.2010.

19.

Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons.

Büsing L, Schrauwen B, Legenstein R.

Neural Comput. 2010 May;22(5):1272-311. doi: 10.1162/neco.2009.01-09-947.

PMID:
20028227
20.

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.

Legenstein R, Chase SM, Schwartz AB, Maass W.

Adv Neural Inf Process Syst. 2009;2009:1105-1113.

21.

Spiking neurons can learn to solve information bottleneck problems and extract independent components.

Klampfl S, Legenstein R, Maass W.

Neural Comput. 2009 Apr;21(4):911-59. doi: 10.1162/neco.2008.01-07-432.

PMID:
19018708
22.

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.

Legenstein R, Pecevski D, Maass W.

PLoS Comput Biol. 2008 Oct;4(10):e1000180. doi: 10.1371/journal.pcbi.1000180. Epub 2008 Oct 10.

23.

On the classification capability of sign-constrained perceptrons.

Legenstein R, Maass W.

Neural Comput. 2008 Jan;20(1):288-309.

PMID:
18045010
24.

Correction of tailor's bunion with the Boesch technique: a retrospective study.

Legenstein R, Bonomo J, Huber W, Boesch P.

Foot Ankle Int. 2007 Jul;28(7):799-803.

PMID:
17666172
25.

Edge of chaos and prediction of computational performance for neural circuit models.

Legenstein R, Maass W.

Neural Netw. 2007 Apr;20(3):323-34. Epub 2007 May 3.

PMID:
17517489
26.

Long-term clinical and radiographic outcome of the PPF system in ceramic-on-polyethylene hip bearings.

Legenstein R, Huber W, Ungersboeck A, Boesch P.

Hip Int. 2006 Apr-Jun;16(2):75-80.

PMID:
19219783
27.

What can a neuron learn with spike-timing-dependent plasticity?

Legenstein R, Naeger C, Maass W.

Neural Comput. 2005 Nov;17(11):2337-82.

PMID:
16156932
28.

Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons.

Legenstein R, Markram H, Maass W.

Rev Neurosci. 2003;14(1-2):5-19. Review.

PMID:
12929914
29.

Indomethacin versus meloxicam for prevention of heterotopic ossification after total hip arthroplasty.

Legenstein R, Bösch P, Ungersböck A.

Arch Orthop Trauma Surg. 2003 Apr;123(2-3):91-4. Epub 2003 Mar 28.

PMID:
12664317
30.

Hallux valgus correction by the method of Bösch: a new technique with a seven-to-ten-year follow-up.

Bösch P, Wanke S, Legenstein R.

Foot Ankle Clin. 2000 Sep;5(3):485-98, v-vi. Review.

PMID:
11232393

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