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Wolfgang Maass: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Zhixiang Chen, Wolfgang Maass
    A Solution of the Credit Assignment Problem in the Case of Learning Rectangles (Abstract). [Citation Graph (0, 0)][DBLP]
    AII, 1992, pp:26-34 [Conf]
  2. Wolfgang Maass
    On the Relevance of Time in Neural Computation and Learning. [Citation Graph (0, 0)][DBLP]
    ALT, 1997, pp:364-384 [Conf]
  3. Prashant Joshi, Wolfgang Maass
    Movement Generation and Control with Generic Neural Microcircuits. [Citation Graph (0, 0)][DBLP]
    BioADIT, 2004, pp:258-273 [Conf]
  4. Wolfgang Maass, Robert A. Legenstein, Henry Markram
    A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models. [Citation Graph (0, 0)][DBLP]
    Biologically Motivated Computer Vision, 2002, pp:282-293 [Conf]
  5. Martin Dietzfelbinger, Wolfgang Maass
    two Lower Bound Arguments with "Inaccessible" Numbers. [Citation Graph (0, 0)][DBLP]
    Structure in Complexity Theory Conference, 1986, pp:163-183 [Conf]
  6. Wolfgang Maass, Georg Schnitger
    An Optimal Lower Bound for Turing Machines with One Work Tape and a Two- way Input Tape. [Citation Graph (0, 0)][DBLP]
    Structure in Complexity Theory Conference, 1986, pp:249-264 [Conf]
  7. Wolfgang Maass, Theodore A. Slaman
    The Complexity Types of Computable Sets. [Citation Graph (0, 0)][DBLP]
    Structure in Complexity Theory Conference, 1989, pp:231-239 [Conf]
  8. Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth
    Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:333-343 [Conf]
  9. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:392-401 [Conf]
  10. William J. Bultman, Wolfgang Maass
    Fast Identification of Geometric Objects with Membership Queries. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:337-353 [Conf]
  11. Zhixiang Chen, Wolfgang Maass
    On-line Learning of Rectangles. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:16-28 [Conf]
  12. Wolfgang Maass
    On-Line Learning with an Oblivious Environment and the Power of Randomization. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:167-175 [Conf]
  13. Wolfgang Maass
    Efficient Agnostic PAC-Learning with Simple Hypothesis. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:67-75 [Conf]
  14. Wolfgang Maass, Michael Schmitt
    On the Complexity of Learning for a Spiking Neuron (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1997, pp:54-61 [Conf]
  15. Wolfgang Maass, György Turán
    On the Complexity of Learning from Counterexamples and Membership Queries (abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:391- [Conf]
  16. Wolfgang Maass
    A Cognitive Model for the Process of Multimodal, Incremental Route Descriptions. [Citation Graph (0, 0)][DBLP]
    COSIT, 1993, pp:1-13 [Conf]
  17. Wolfgang Maass
    How Spatial Information Connects Visual Perception and Natural Language Generation in Dynamic Environments: Towards a Computational Model. [Citation Graph (0, 0)][DBLP]
    COSIT, 1995, pp:223-240 [Conf]
  18. Wolfgang Maass, Gerald Steinbauer, Roland Koholka
    Autonomous Fast Learning in a Mobile Robot. [Citation Graph (0, 0)][DBLP]
    Sensor Based Intelligent Robots, 2000, pp:345-356 [Conf]
  19. Thomas Natschläger, Wolfgang Maass
    Fast analog computation in networks of spiking neurons using unreliable synapses. [Citation Graph (0, 0)][DBLP]
    ESANN, 1999, pp:417-422 [Conf]
  20. Wolfgang Maass, Theodore A. Slaman
    Extensional Properties of Sets of Time Bounded Complexity (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    FCT, 1989, pp:318-326 [Conf]
  21. Noga Alon, Wolfgang Maass
    Meanders, Ramsey Theory and Lower Bounds for Branching Programs [Citation Graph (0, 0)][DBLP]
    FOCS, 1986, pp:410-417 [Conf]
  22. András Hajnal, Wolfgang Maass, Pavel Pudlák, Mario Szegedy, György Turán
    Threshold circuits of bounded depth [Citation Graph (0, 0)][DBLP]
    FOCS, 1987, pp:99-110 [Conf]
  23. Wolfgang Maass, Georg Schnitger, Eduardo D. Sontag
    On the Computational Power of Sigmoid versus Boolean Threshold Circuits [Citation Graph (0, 0)][DBLP]
    FOCS, 1991, pp:767-776 [Conf]
  24. Wolfgang Maass, György Turán
    On the Complexity of Learning From Counterexamples (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1989, pp:262-267 [Conf]
  25. Wolfgang Maass, György Turán
    On the Complexity of Learning from Counterexamples and Membership Queries [Citation Graph (0, 0)][DBLP]
    FOCS, 1990, pp:203-210 [Conf]
  26. Wolfgang Maass, Andreas Filler
    Towards an Infrastructure for Semantically Annotated Physical Products. [Citation Graph (0, 0)][DBLP]
    GI Jahrestagung (2), 2006, pp:544-549 [Conf]
  27. Martin Dietzfelbinger, Wolfgang Maass
    The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape. [Citation Graph (0, 0)][DBLP]
    ICALP, 1988, pp:188-200 [Conf]
  28. Kei Uchizawa, Rodney J. Douglas, Wolfgang Maass
    Energy Complexity and Entropy of Threshold Circuits. [Citation Graph (0, 0)][DBLP]
    ICALP (1), 2006, pp:631-642 [Conf]
  29. Peter Auer, Harald Burgsteiner, Wolfgang Maass
    Reducing Communication for Distributed Learning in Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:123-128 [Conf]
  30. Wolfgang Maass
    On the Computational Power of Neural Microcircuit Models: Pointers to the Literature. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:254-258 [Conf]
  31. Peter Auer, Robert C. Holte, Wolfgang Maass
    Theory and Applications of Agnostic PAC-Learning with Small Decision Trees. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:21-29 [Conf]
  32. Wolfgang Maass, Manfred K. Warmuth
    Efficient Learning with Virtual Threshold Gates. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:378-386 [Conf]
  33. Wolfgang Maass
    Models for Fast Analog Computation with Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    ICONIP, 1998, pp:187-188 [Conf]
  34. Wolfgang Maass
    On the Role of Time and Space in Neural Computation. [Citation Graph (0, 0)][DBLP]
    MFCS, 1998, pp:72-83 [Conf]
  35. Wolfgang Maass
    Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    NC, 1998, pp:16-20 [Conf]
  36. Ajay Gupta, Wolfgang Maass
    Efficient Design of Boltzmann Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:825-831 [Conf]
  37. Robert A. Legenstein, Wolfgang Maass
    Foundations for a Circuit Complexity Theory of Sensory Processing. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:259-265 [Conf]
  38. Robert A. Legenstein, Wolfgang Maass
    A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  39. Wolfgang Maass, Eduardo D. Sontag
    A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:281-287 [Conf]
  40. Wolfgang Maass, Pekka Orponen
    On the Effect of Analog Noise in Discrete-Time Analog Computations. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:218-224 [Conf]
  41. Wolfgang Maass, Anthony M. Zador
    Dynamic Stochastic Synapses as Computational Units. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  42. Wolfgang Maass
    Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:211-217 [Conf]
  43. Wolfgang Maass
    Neural Computation with Winner-Take-All as the Only Nonlinear Operation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:293-299 [Conf]
  44. Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag
    Principles of real-time computing with feedback applied to cortical microcircuit models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  45. Wolfgang Maass, Robert A. Legenstein, Nils Bertschinger
    Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  46. Wolfgang Maass
    Agnostic PAC-Learning of Functions on Analog Neural Nets. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:311-318 [Conf]
  47. Wolfgang Maass
    On the Computational Complexity of Networks of Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:183-190 [Conf]
  48. Wolfgang Maass
    On the Computational Power of Noisy Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:211-217 [Conf]
  49. Wolfgang Maass, Thomas Natschläger, Henry Markram
    A Model for Real-Time Computation in Generic Neural Microcircuits. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:213-220 [Conf]
  50. Thomas Natschläger, Wolfgang Maass
    Finding the Key to a Synapse. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:138-144 [Conf]
  51. Thomas Natschläger, Wolfgang Maass
    Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  52. Thomas Natschläger, Wolfgang Maass, Eduardo D. Sontag, Anthony M. Zador
    Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:145-151 [Conf]
  53. Dimitris Apostolou, Gregoris Mentzas, Andreas Abecker, Wolf-Christian Eickhoff, Wolfgang Maas, Panos Georgolios, Kostas Kafentzis, Sophia Kyriakopoulou
    Challenges and Directions in Knowledge Asset Trading. [Citation Graph (0, 0)][DBLP]
    PAKM, 2002, pp:549-564 [Conf]
  54. Dorit S. Hochbaum, Wolfgang Maass
    Approximation Schemes for Covering and Packing Problems in Robotics and VLSI. [Citation Graph (0, 0)][DBLP]
    STACS, 1984, pp:55-62 [Conf]
  55. András Hajnal, Wolfgang Maass, György Turán
    On the Communication Complexity of Graph Properties [Citation Graph (0, 0)][DBLP]
    STOC, 1988, pp:186-191 [Conf]
  56. Wolfgang Maass
    Quadratic Lower Bounds for Deterministic and Nondeterministic One-Tape Turing Machines (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    STOC, 1984, pp:401-408 [Conf]
  57. Wolfgang Maass
    Bounds for the computational power and learning complexity of analog neural nets. [Citation Graph (0, 0)][DBLP]
    STOC, 1993, pp:335-344 [Conf]
  58. Wolfgang Maass, Georg Schnitger, Endre Szemerédi
    Two Tapes Are Better than One for Off-Line Turing Machines [Citation Graph (0, 0)][DBLP]
    STOC, 1987, pp:94-100 [Conf]
  59. Winfried Graf, Wolfgang Maass
    Constraint-basierte Verarbeitung graphischen Wissens. [Citation Graph (0, 0)][DBLP]
    Wissensbasierte Systeme, 1991, pp:243-253 [Conf]
  60. Klaus Sutner, Wolfgang Maass
    Motion Planning Among Time Dependent Obstacles. [Citation Graph (0, 0)][DBLP]
    Acta Inf., 1988, v:26, n:1/2, pp:93-122 [Journal]
  61. Wolfgang Maass
    From Vision to Multimodal Communication: Incremental Route Descriptions. [Citation Graph (0, 0)][DBLP]
    Artif. Intell. Rev., 1994, v:8, n:2-3, pp:159-174 [Journal]
  62. Wolfgang Maass, Georg Schnitger, Endre Szemerédi, György Turán
    Two Tapes Versus One for Off-Line Turing Machines. [Citation Graph (0, 0)][DBLP]
    Computational Complexity, 1993, v:3, n:, pp:392-401 [Journal]
  63. Wolfgang Maass
    Neural Computation: A Research Topic for Theoretical Computer Science? Some Thoughts and Pointers. [Citation Graph (0, 0)][DBLP]
    Bulletin of the EATCS, 2000, v:72, n:, pp:149-158 [Journal]
  64. Wolfgang Maass
    A Simple Model for Neural Computation with Firing Rates and Firing Correlations [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:30, pp:- [Journal]
  65. Wolfgang Maass, Eduardo D. Sontag
    Neural Systems as Nonlinear Filters [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:31, pp:- [Journal]
  66. Wolfgang Maass
    On the Computational Power of Winner-Take-All [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:32, pp:- [Journal]
  67. Wolfgang Maass
    On Computation with Pulses [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:38, pp:- [Journal]
  68. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:50, pp:- [Journal]
  69. Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth
    Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:55, pp:- [Journal]
  70. Robert A. Legenstein, Wolfgang Maass
    Optimizing the Layout of a Balanced Tree [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2001, v:8, n:069, pp:- [Journal]
  71. Robert A. Legenstein, Wolfgang Maass
    Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2001, v:8, n:070, pp:- [Journal]
  72. Robert A. Legenstein, Wolfgang Maass
    Neural Circuits for Pattern Recognition with Small Total Wire Length [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2001, v:8, n:071, pp:- [Journal]
  73. Wolfgang Maass, Henry Markram
    On the Computational Power of Recurrent Circuits of Spiking Neurons [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2002, v:, n:022, pp:- [Journal]
  74. Wolfgang Maass
    Bounds for the Computational Power and Learning Complexity of Analog Neural Nets [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1994, v:1, n:12, pp:- [Journal]
  75. Wolfgang Maass
    Neural Nets with Superlinear VC-Dimension [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1994, v:1, n:17, pp:- [Journal]
  76. Wolfgang Maass
    Lower Bounds for the Computational Power of Networks of Spiking Neurons [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1994, v:1, n:19, pp:- [Journal]
  77. Wolfgang Maass
    Agnostic PAC-Learning of Functions on Analog Neural Nets [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1994, v:1, n:20, pp:- [Journal]
  78. David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass
    Computing the Maximum Bichromatic Discrepancy, with applications to Computer Graphics and Machine Learning [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1994, v:1, n:25, pp:- [Journal]
  79. Wolfgang Maass
    Networks of Spiking Neurons: The Third Generation of Neural Network Models [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 1996, v:3, n:31, pp:- [Journal]
  80. Wolfgang Maass, Jörg P. Müller
    Preface to the Special Section on Software Agents. [Citation Graph (0, 0)][DBLP]
    Electronic Markets, 2003, v:13, n:1, pp:- [Journal]
  81. William J. Bultman, Wolfgang Maass
    Fast Identification of Geometric Objects with Membership Queries [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1995, v:118, n:1, pp:48-64 [Journal]
  82. Wolfgang Maass, Berthold Ruf
    On Computations with Pulses. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1999, v:148, n:2, pp:202-218 [Journal]
  83. Wolfgang Maass, Michael Schmitt
    On the Complexity of Learning for Spiking Neurons with Temporal Coding. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1999, v:153, n:1, pp:26-46 [Journal]
  84. Wolfgang Maass, Manfred K. Warmuth
    Efficient Learning With Virtual Threshold Gates. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1998, v:141, n:1, pp:66-83 [Journal]
  85. Dorit S. Hochbaum, Wolfgang Maass
    Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI [Citation Graph (0, 0)][DBLP]
    J. ACM, 1985, v:32, n:1, pp:130-136 [Journal]
  86. Dorit S. Hochbaum, Wolfgang Maass
    Fast Approximation Algorithms for a Nonconvex Covering Problem. [Citation Graph (0, 0)][DBLP]
    J. Algorithms, 1987, v:8, n:3, pp:305-323 [Journal]
  87. Noga Alon, Wolfgang Maass
    Meanders and Their Applications in Lower Bounds Arguments. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1988, v:37, n:2, pp:118-129 [Journal]
  88. Martin Dietzfelbinger, Wolfgang Maass
    Lower Bound Arguments with "Inaccessible" Numbers. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1988, v:36, n:3, pp:313-335 [Journal]
  89. David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass
    Computing the Maximum Bichromatic Discrepancy with Applications to Computer Graphics and Machine Learning. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1996, v:52, n:3, pp:453-470 [Journal]
  90. András Hajnal, Wolfgang Maass, Pavel Pudlák, Mario Szegedy, György Turán
    Threshold Circuits of Bounded Depth. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1993, v:46, n:2, pp:129-154 [Journal]
  91. Robert A. Legenstein, Wolfgang Maass
    Wire length as a circuit complexity measure. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2005, v:70, n:1, pp:53-72 [Journal]
  92. Wolfgang Maass
    Editor's Foreword. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1995, v:51, n:3, pp:339- [Journal]
  93. Wolfgang Maass, Henry Markram
    On the computational power of circuits of spiking neurons. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2004, v:69, n:4, pp:593-616 [Journal]
  94. Wolfgang Maass, Theodore A. Slaman
    The Complexity Types of Computable Sets. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1992, v:44, n:2, pp:168-192 [Journal]
  95. Wolfgang Maass
    On the Use of Inaccessible Numbers and Order Indiscernibles in Lower Bound Arguments for Random Access Machines. [Citation Graph (0, 0)][DBLP]
    J. Symb. Log., 1988, v:53, n:4, pp:1098-1109 [Journal]
  96. Wolfgang Maass
    Variations on Promptly Simple Sets. [Citation Graph (0, 0)][DBLP]
    J. Symb. Log., 1985, v:50, n:1, pp:138-148 [Journal]
  97. Wolfgang Maass
    The Uniform Regular Set Theorem in a-Recursion Theory. [Citation Graph (0, 0)][DBLP]
    J. Symb. Log., 1978, v:43, n:2, pp:270-279 [Journal]
  98. Wolfgang Maass
    Recursively Enumerable Generic Sets. [Citation Graph (0, 0)][DBLP]
    J. Symb. Log., 1982, v:47, n:4, pp:809-823 [Journal]
  99. Wolfgang Maass
    On the Orbits of Hyperhypersimple Sets. [Citation Graph (0, 0)][DBLP]
    J. Symb. Log., 1984, v:49, n:1, pp:51-62 [Journal]
  100. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:18, n:2-3, pp:187-230 [Journal]
  101. Zhixiang Chen, Wolfgang Maass
    On-Line Learning of Rectangles and Unions of Rectangles. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:17, n:2-3, pp:201-223 [Journal]
  102. Wolfgang Maass, György Turán
    Lower Bound Methods and Separation Results for On-Line Learning Models. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1992, v:9, n:, pp:107-145 [Journal]
  103. Wolfgang Maass, György Turán
    Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:14, n:1, pp:251-269 [Journal]
  104. Prashant Joshi, Wolfgang Maass
    Movement Generation with Circuits of Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2005, v:17, n:8, pp:1715-1738 [Journal]
  105. Robert A. Legenstein, Christian Naeger, Wolfgang Maass
    What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2005, v:17, n:11, pp:2337-2382 [Journal]
  106. Wolfgang Maass
    On the Computational Power of Winner-Take-All. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:11, pp:2519-2535 [Journal]
  107. Wolfgang Maass
    Fast Sigmoidal Networks via Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:2, pp:279-304 [Journal]
  108. Wolfgang Maass, Thomas Natschläger
    A Model for Fast Analog Computation Based on Unreliable Synapses. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:7, pp:1679-1704 [Journal]
  109. Wolfgang Maass, Thomas Natschläger, Henry Markram
    Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:11, pp:2531-2560 [Journal]
  110. Wolfgang Maass, Pekka Orponen
    On the Effect of Analog Noise in Discrete-Time Analog Computations. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:5, pp:1071-1095 [Journal]
  111. Wolfgang Maass, Eduardo D. Sontag
    Neural Systems as Nonlinear Filters. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:8, pp:1743-1772 [Journal]
  112. Wolfgang Maass, Eduardo D. Sontag
    Analog Neural Nets with Gaussian or Other Common Noise Distribution Cannot Recognize Arbitrary Regular Languages. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1999, v:11, n:3, pp:771-782 [Journal]
  113. Wolfgang Maass, Anthony M. Zador
    Dynamic Stochastic Synapses as Computational Units. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1999, v:11, n:4, pp:903-917 [Journal]
  114. Thomas Natschläger, Wolfgang Maass
    Computing the Optimally Fitted Spike Train for a Synapse. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:13, n:11, pp:2477-2494 [Journal]
  115. Kei Uchizawa, Rodney J. Douglas, Wolfgang Maass
    On the Computational Power of Threshold Circuits with Sparse Activity. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2006, v:18, n:12, pp:2994-3008 [Journal]
  116. Stephen Grossberg, Wolfgang Maass, Henry Markram
    Introduction: Spiking Neurons in Neuroscience and Technology. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2001, v:14, n:6-7, pp:587-0 [Journal]
  117. Wolfgang Maass
    Networks of spiking neurons: The third generation of neural network models. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:9, pp:1659-1671 [Journal]
  118. Wolfgang Maass, Henry Markram
    Synapses as dynamic memory buffers. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2002, v:15, n:1, pp:155-161 [Journal]
  119. Thomas Natschläger, Wolfgang Maass
    Dynamics of information and emergent computation in generic neural microcircuit models. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:10, pp:1301-1308 [Journal]
  120. Alexander Kaske, Wolfgang Maass
    A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2006, v:19, n:5, pp:600-609 [Journal]
  121. Wolfgang Maass
    "Imitation of life: how biology is inspiring computing" by Nancy Forbes. [Citation Graph (0, 0)][DBLP]
    Pattern Anal. Appl., 2006, v:8, n:4, pp:390-391 [Journal]
  122. Wolfgang Maass
    On the Complexity of Nonconvex Covering. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1986, v:15, n:2, pp:453-467 [Journal]
  123. Wolfgang Maass
    Bounds for the Computational Power and Learning Complexity of Analog Neural Nets. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1997, v:26, n:3, pp:708-732 [Journal]
  124. Wolfgang Maass, Amir Schorr
    Speed-Up of Turing Machines with One Work Tape and a Two-Way Input Tape. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1987, v:16, n:1, pp:195-202 [Journal]
  125. Martin Dietzfelbinger, Wolfgang Maass
    The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1993, v:108, n:2, pp:271-290 [Journal]
  126. Martin Dietzfelbinger, Wolfgang Maass, Georg Schnitger
    The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1991, v:82, n:1, pp:113-129 [Journal]
  127. Steven Homer, Wolfgang Maass
    Oracle-Dependent Properties of the Lattice of NP Sets. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1983, v:24, n:, pp:279-289 [Journal]
  128. Robert A. Legenstein, Wolfgang Maass
    Neural circuits for pattern recognition with small total wire length. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2002, v:287, n:1, pp:239-249 [Journal]
  129. Wolfgang Maass
    On the relevance of time in neural computation and learning. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2001, v:261, n:1, pp:157-178 [Journal]
  130. Thomas Natschläger, Wolfgang Maass
    Spiking neurons and the induction of finite state machines. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2002, v:287, n:1, pp:251-265 [Journal]
  131. Wolfgang Maass
    Liquid Computing. [Citation Graph (0, 0)][DBLP]
    CiE, 2007, pp:507-516 [Conf]
  132. Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
    Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:250-261 [Conf]
  133. Danko Nikolic, Stefan Haeusler, Wolf Singer, Wolfgang Maass
    Temporal dynamics of information content carried by neurons in the primary visual cortex. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1041-1048 [Conf]
  134. Stefan Klampfl, Robert A. Legenstein, Wolfgang Maass
    Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:713-720 [Conf]
  135. Robert A. Legenstein, Wolfgang Maass
    Edge of chaos and prediction of computational performance for neural circuit models. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:3, pp:323-334 [Journal]
  136. Herbert Jaeger, Wolfgang Maass, José Carlos Príncipe
    Special issue on echo state networks and liquid state machines. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:3, pp:287-289 [Journal]

  137. 08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications. [Citation Graph (, )][DBLP]


  138. 08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications. [Citation Graph (, )][DBLP]


  139. Integration of Standardized and Non-Standardized Product Data. [Citation Graph (, )][DBLP]


  140. Learning complex motions by sequencing simpler motion templates. [Citation Graph (, )][DBLP]


  141. Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity. [Citation Graph (, )][DBLP]


  142. Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. [Citation Graph (, )][DBLP]


  143. Hebbian Learning of Bayes Optimal Decisions. [Citation Graph (, )][DBLP]


  144. Vocabulary Patterns in Free-for-all Collaborative Indexing Systems. [Citation Graph (, )][DBLP]


  145. Ontology-Based Natural Language Processing for In-store Shopping Situations. [Citation Graph (, )][DBLP]


  146. Product-Centered Mobile Reasoning Support for Physical Shopping Situations. [Citation Graph (, )][DBLP]


  147. A Methodology for Content-Centered Design of Ambient Environments. [Citation Graph (, )][DBLP]


  148. Computational aspects of feedback in neural circuits. [Citation Graph (, )][DBLP]


  149. Energy Complexity and Entropy of Threshold Circuits. [Citation Graph (, )][DBLP]


  150. Adoption and Diffusion in Electronic Markets: An Empirical Analysis of Attributes Influencing the Adoption of Paid Content. [Citation Graph (, )][DBLP]


  151. Preface to the Focus Theme Section: 'Smart Products'. [Citation Graph (, )][DBLP]


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