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Items: 1 to 50 of 89

1.

Neuromorphic Hardware Learns to Learn.

Bohnstingl T, Scherr F, Pehle C, Meier K, Maass W.

Front Neurosci. 2019 May 21;13:483. doi: 10.3389/fnins.2019.00483. eCollection 2019.

2.

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
3.

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.

4.

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.

5.

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.

6.

Energy-efficient neural network chips approach human recognition capabilities.

Maass W.

Proc Natl Acad Sci U S A. 2016 Oct 11;113(41):11387-11389. Epub 2016 Oct 4. No abstract available.

7.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

Pecevski D, Maass W.

eNeuro. 2016 Jun 21;3(2). pii: ENEURO.0048-15.2016. doi: 10.1523/ENEURO.0048-15.2016. eCollection 2016 Mar-Apr.

8.

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

Jonke Z, Habenschuss S, Maass W.

Front Neurosci. 2016 Mar 30;10:118. doi: 10.3389/fnins.2016.00118. eCollection 2016.

9.

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.

10.

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.

11.

A behavioral and histological comparison of fluid percussion injury and controlled cortical impact injury to the rat sensorimotor cortex.

Peterson TC, Maass WR, Anderson JR, Anderson GD, Hoane MR.

Behav Brain Res. 2015 Nov 1;294:254-63. doi: 10.1016/j.bbr.2015.08.007. Epub 2015 Aug 12.

12.

Acute moderate exercise does not attenuate cardiometabolic function associated with a bout of prolonged sitting.

Younger AM, Pettitt RW, Sexton PJ, Maass WJ, Pettitt CD.

J Sports Sci. 2016;34(7):658-63. doi: 10.1080/02640414.2015.1068435. Epub 2015 Jul 17.

PMID:
26186044
13.

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.

14.

Deficits in discrimination after experimental frontal brain injury are mediated by motivation and can be improved by nicotinamide administration.

Vonder Haar C, Maass WR, Jacobs EA, Hoane MR.

J Neurotrauma. 2014 Oct 15;31(20):1711-20. doi: 10.1089/neu.2014.3459. Epub 2014 Aug 21.

15.

STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning.

Kappel D, Nessler B, Maass W.

PLoS Comput Biol. 2014 Mar 27;10(3):e1003511. doi: 10.1371/journal.pcbi.1003511. eCollection 2014 Mar.

16.

Cardiac monitoring in patients with electrical injuries. An analysis of 268 patients at the Charité Hospital.

Searle J, Slagman A, Maaß W, Möckel M.

Dtsch Arztebl Int. 2013 Dec 13;110(50):847-53. doi: 10.3238/arztebl.2013.0847.

17.

Stochastic computations in cortical microcircuit models.

Habenschuss S, Jonke Z, Maass W.

PLoS Comput Biol. 2013;9(11):e1003311. doi: 10.1371/journal.pcbi.1003311. Epub 2013 Nov 14.

18.

Emergence of dynamic memory traces in cortical microcircuit models through STDP.

Klampfl S, Maass W.

J Neurosci. 2013 Jul 10;33(28):11515-29. doi: 10.1523/JNEUROSCI.5044-12.2013.

19.

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

Nessler B, Pfeiffer M, Buesing L, Maass W.

PLoS Comput Biol. 2013 Apr;9(4):e1003037. doi: 10.1371/journal.pcbi.1003037. Epub 2013 Apr 25.

20.

Emergence of optimal decoding of population codes through STDP.

Habenschuss S, Puhr H, Maass W.

Neural Comput. 2013 Jun;25(6):1371-407. doi: 10.1162/NECO_a_00446. Epub 2013 Mar 21.

PMID:
23517096
21.

Learned graphical models for probabilistic planning provide a new class of movement primitives.

Rückert EA, Neumann G, Toussaint M, Maass W.

Front Comput Neurosci. 2013 Jan 2;6:97. doi: 10.3389/fncom.2012.00097. eCollection 2012.

22.

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
23.

The role of feedback in morphological computation with compliant bodies.

Hauser H, Ijspeert AJ, Füchslin RM, Pfeifer R, Maass W.

Biol Cybern. 2012 Nov;106(10):595-613. doi: 10.1007/s00422-012-0516-4. Epub 2012 Sep 6.

PMID:
22956025
24.

Probing real sensory worlds of receivers with unsupervised clustering.

Pfeiffer M, Hartbauer M, Lang AB, Maass W, Römer H.

PLoS One. 2012;7(6):e37354. doi: 10.1371/journal.pone.0037354. Epub 2012 Jun 6.

25.

A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons.

Klampfl S, David SV, Yin P, Shamma SA, Maass W.

J Neurophysiol. 2012 Sep;108(5):1366-80. doi: 10.1152/jn.00935.2011. Epub 2012 Jun 13.

26.

Fractal MapReduce decomposition of sequence alignment.

Almeida JS, Grüneberg A, Maass W, Vinga S.

Algorithms Mol Biol. 2012 May 2;7(1):12. doi: 10.1186/1748-7188-7-12.

27.

Towards a theoretical foundation for morphological computation with compliant bodies.

Hauser H, Ijspeert AJ, Füchslin RM, Pfeifer R, Maass W.

Biol Cybern. 2011 Dec;105(5-6):355-70. doi: 10.1007/s00422-012-0471-0. Epub 2012 Jan 31.

PMID:
22290137
28.

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Pecevski D, Buesing L, Maass W.

PLoS Comput Biol. 2011 Dec;7(12):e1002294. doi: 10.1371/journal.pcbi.1002294. Epub 2011 Dec 15.

29.

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

Buesing L, Bill J, Nessler B, Maass W.

PLoS Comput Biol. 2011 Nov;7(11):e1002211. doi: 10.1371/journal.pcbi.1002211. Epub 2011 Nov 3.

30.

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.

31.

S3QL: a distributed domain specific language for controlled semantic integration of life sciences data.

Deus HF, Correa MC, Stanislaus R, Miragaia M, Maass W, de Lencastre H, Fox R, Almeida JS.

BMC Bioinformatics. 2011 Jul 14;12:285. doi: 10.1186/1471-2105-12-285.

32.

Biologically inspired kinematic synergies enable linear balance control of a humanoid robot.

Hauser H, Neumann G, Ijspeert AJ, Maass W.

Biol Cybern. 2011 May;104(4-5):235-49. doi: 10.1007/s00422-011-0430-1. Epub 2011 Apr 27.

PMID:
21523489
33.

Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1.

Rasch MJ, Schuch K, Logothetis NK, Maass W.

J Neurophysiol. 2011 Feb;105(2):757-78. doi: 10.1152/jn.00845.2009. Epub 2010 Nov 24.

34.

Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity.

Bill J, Schuch K, Brüderle D, Schemmel J, Maass W, Meier K.

Front Comput Neurosci. 2010 Oct 8;4:129. doi: 10.3389/fncom.2010.00129. eCollection 2010.

35.

A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction.

Klampfl S, Maass W.

Neural Comput. 2010 Dec;22(12):2979-3035. doi: 10.1162/NECO_a_00050. Epub 2010 Sep 21.

PMID:
20858129
36.

S3DB core: a framework for RDF generation and management in bioinformatics infrastructures.

Almeida JS, Deus HF, Maass W.

BMC Bioinformatics. 2010 Jul 20;11:387. doi: 10.1186/1471-2105-11-387.

37.

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.

38.

Influence of pinholes on MgO-tunnel junction barrier parameters obtained from current-voltage characteristics.

Ventura J, Teixeira JM, Araujo JP, Sousa JB, Ferreira R, Freitas PP, Langer J, Ocker B, Maass W.

J Nanosci Nanotechnol. 2010 Apr;10(4):2731-4.

PMID:
20355492
39.

A spiking neuron as information bottleneck.

Buesing L, Maass W.

Neural Comput. 2010 Aug;22(8):1961-92. doi: 10.1162/neco.2010.08-09-1084.

PMID:
20337537
40.

Reward-modulated Hebbian learning of decision making.

Pfeiffer M, Nessler B, Douglas RJ, Maass W.

Neural Comput. 2010 Jun;22(6):1399-444. doi: 10.1162/neco.2010.03-09-980.

PMID:
20141476
41.

Distributed fading memory for stimulus properties in the primary visual cortex.

Nikolić D, Häusler S, Singer W, Maass W.

PLoS Biol. 2009 Dec;7(12):e1000260. doi: 10.1371/journal.pbio.1000260. Epub 2009 Dec 22.

42.

Belief propagation in networks of spiking neurons.

Steimer A, Maass W, Douglas R.

Neural Comput. 2009 Sep;21(9):2502-23. doi: 10.1162/neco.2009.08-08-837.

43.

Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates.

Haeusler S, Schuch K, Maass W.

J Physiol Paris. 2009 Jan-Mar;103(1-2):73-87. doi: 10.1016/j.jphysparis.2009.05.006. Epub 2009 Jun 11.

PMID:
19500669
44.

State-dependent computations: spatiotemporal processing in cortical networks.

Buonomano DV, Maass W.

Nat Rev Neurosci. 2009 Feb;10(2):113-25. doi: 10.1038/nrn2558. Epub 2009 Jan 15. Review.

PMID:
19145235
45.

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.

46.

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
47.

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.

48.

A learning rule for very simple universal approximators consisting of a single layer of perceptrons.

Auer P, Burgsteiner H, Maass W.

Neural Netw. 2008 Jun;21(5):786-95. doi: 10.1016/j.neunet.2007.12.036. Epub 2007 Dec 31.

PMID:
18249524
49.

Inferring spike trains from local field potentials.

Rasch MJ, Gretton A, Murayama Y, Maass W, Logothetis NK.

J Neurophysiol. 2008 Mar;99(3):1461-76. Epub 2007 Dec 26.

50.

On the classification capability of sign-constrained perceptrons.

Legenstein R, Maass W.

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

PMID:
18045010

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