Wolfgang Maass

According to our database1, Wolfgang Maass
  • authored at least 188 papers between 1977 and 2017.
  • has a "Dijkstra number"2 of four.

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Bibliography

2017
Deep Rewiring: Training very sparse deep networks.
CoRR, 2017

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System.
CoRR, 2017

Pattern representation and recognition with accelerated analog neuromorphic systems.
CoRR, 2017

Reward-based stochastic self-configuration of neural circuits.
CoRR, 2017



2016
Hamiltonian synaptic sampling in a model for reward-gated network plasticity.
CoRR, 2016

Variable Binding through Assemblies in Spiking Neural Networks.
Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), 2016

2015
Network Plasticity as Bayesian Inference.
PLoS Computational Biology, 2015

To Spike or Not to Spike: That Is the Question.
Proceedings of the IEEE, 2015

Network Plasticity as Bayesian Inference.
CoRR, 2015

Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment.
PLoS Computational Biology, 2014

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning.
PLoS Computational Biology, 2014

Noise as a Resource for Computation and Learning in Networks of Spiking Neurons.
Proceedings of the IEEE, 2014

A theoretical basis for efficient computations with noisy spiking neurons.
CoRR, 2014

2013
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity.
PLoS Computational Biology, 2013

Stochastic Computations in Cortical Microcircuit Models.
PLoS Computational Biology, 2013

Emergence of Optimal Decoding of Population Codes Through STDP.
Neural Computation, 2013

Learned graphical models for probabilistic planning provide a new class of movement primitives.
Front. Comput. Neurosci., 2013

2012
The role of feedback in morphological computation with compliant bodies.
Biological Cybernetics, 2012

Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

2011
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons.
PLoS Computational Biology, 2011

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons.
PLoS Computational Biology, 2011

Biologically inspired kinematic synergies enable linear balance control of a humanoid robot.
Biological Cybernetics, 2011

Towards a theoretical foundation for morphological computation with compliant bodies.
Biological Cybernetics, 2011

2010
Reward-Modulated Hebbian Learning of Decision Making.
Neural Computation, 2010

A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction.
Neural Computation, 2010

A Spiking Neuron as Information Bottleneck.
Neural Computation, 2010

Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity.
Front. Comput. Neurosci., 2010

10302 Summary - Learning paradigms in dynamic environments.
Proceedings of the Learning paradigms in dynamic environments, 25.07. - 30.07.2010, 2010

10302 Abstracts Collection - Learning paradigms in dynamic environments.
Proceedings of the Learning paradigms in dynamic environments, 25.07. - 30.07.2010, 2010

2009
Belief Propagation in Networks of Spiking Neurons.
Neural Computation, 2009

Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components.
Neural Computation, 2009

STDP enables spiking neurons to detect hidden causes of their inputs.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Learning complex motions by sequencing simpler motion templates.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback.
PLoS Computational Biology, 2008

A learning rule for very simple universal approximators consisting of a single layer of perceptrons.
Neural Networks, 2008

On the Classification Capability of Sign-Constrained Perceptrons.
Neural Computation, 2008

Hebbian Learning of Bayes Optimal Decisions.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications.
Proceedings of the Recurrent Neural Networks - Models, Capacities, and Applications, 20.01., 2008

08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications.
Proceedings of the Recurrent Neural Networks - Models, Capacities, and Applications, 20.01., 2008

2007
Computational Aspects of Feedback in Neural Circuits.
PLoS Computational Biology, 2007

Edge of chaos and prediction of computational performance for neural circuit models.
Neural Networks, 2007

Special issue on echo state networks and liquid state machines.
Neural Networks, 2007

Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Biologically inspired kinematic synergies provide a new paradigm for balance control of humanoid robots.
Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots, November 29th, 2007

Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs.
Proceedings of the Machine Learning: ECML 2007, 2007

Liquid Computing.
Proceedings of the Computation and Logic in the Real World, 2007

2006
"Imitation of life: how biology is inspiring computing" by Nancy Forbes.
Pattern Anal. Appl., 2006

A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models.
Neural Networks, 2006

On the Computational Power of Threshold Circuits with Sparse Activity.
Neural Computation, 2006

Energy Complexity and Entropy of Threshold Circuits.
Electronic Colloquium on Computational Complexity (ECCC), 2006

Computational aspects of feedback in neural circuits.
Electronic Colloquium on Computational Complexity (ECCC), 2006

Temporal dynamics of information content carried by neurons in the primary visual cortex.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Energy Complexity and Entropy of Threshold Circuits.
Proceedings of the Automata, Languages and Programming, 33rd International Colloquium, 2006

2005
Dynamics of information and emergent computation in generic neural microcircuit models.
Neural Networks, 2005

What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation, 2005

Movement Generation with Circuits of Spiking Neurons.
Neural Computation, 2005

Wire length as a circuit complexity measure.
J. Comput. Syst. Sci., 2005

Principles of real-time computing with feedback applied to cortical microcircuit models.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004
On the computational power of circuits of spiking neurons.
J. Comput. Syst. Sci., 2004

Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Movement Generation and Control with Generic Neural Microcircuits.
Proceedings of the Biologically Inspired Approaches to Advanced Information Technology, 2004

2003
Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts.
Complexity, 2003

Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2002
Spiking neurons and the induction of finite state machines.
Theor. Comput. Sci., 2002

Neural circuits for pattern recognition with small total wire length.
Theor. Comput. Sci., 2002

Synapses as dynamic memory buffers.
Neural Networks, 2002

Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations.
Neural Computation, 2002

On the Computational Power of Recurrent Circuits of Spiking Neurons
Electronic Colloquium on Computational Complexity (ECCC), 2002

A Model for Real-Time Computation in Generic Neural Microcircuits.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

On the Computational Power of Neural Microcircuit Models: Pointers to the Literature.
Proceedings of the Artificial Neural Networks, 2002

Reducing Communication for Distributed Learning in Neural Networks.
Proceedings of the Artificial Neural Networks, 2002

A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models.
Proceedings of the Biologically Motivated Computer Vision Second International Workshop, 2002

2001
On the relevance of time in neural computation and learning.
Theor. Comput. Sci., 2001

Introduction: Spiking Neurons in Neuroscience and Technology.
Neural Networks, 2001

Computing the Optimally Fitted Spike Train for a Synapse.
Neural Computation, 2001

Neural Circuits for Pattern Recognition with Small Total Wire Length
Electronic Colloquium on Computational Complexity (ECCC), 2001

Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing
Electronic Colloquium on Computational Complexity (ECCC), 2001

Optimizing the Layout of a Balanced Tree
Electronic Colloquium on Computational Complexity (ECCC), 2001

Neural Computation: A Research Topic for Theoretical Computer Science? Some Thoughts and Pointers.
Current Trends in Theoretical Computer Science, 2001

2000
Neural Systems as Nonlinear Filters.
Neural Computation, 2000

A Model for Fast Analog Computation Based on Unreliable Synapses.
Neural Computation, 2000

On the Computational Power of Winner-Take-All.
Neural Computation, 2000

Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes
Electronic Colloquium on Computational Complexity (ECCC), 2000

On the Complexity of Function Learning
Electronic Colloquium on Computational Complexity (ECCC), 2000

On Computation with Pulses
Electronic Colloquium on Computational Complexity (ECCC), 2000

On the Computational Power of Winner-Take-All
Electronic Colloquium on Computational Complexity (ECCC), 2000

Neural Systems as Nonlinear Filters
Electronic Colloquium on Computational Complexity (ECCC), 2000

A Simple Model for Neural Computation with Firing Rates and Firing Correlations.
Electronic Colloquium on Computational Complexity (ECCC), 2000

Neural Computation: A Research Topic for Theoretical Computer Science? Some Thoughts and Pointers.
Bulletin of the EATCS, 2000

Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Finding the Key to a Synapse.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Foundations for a Circuit Complexity Theory of Sensory Processing.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Autonomous Fast Learning in a Mobile Robot.
Proceedings of the Sensor Based Intelligent Robots, 2000

1999
Dynamic Stochastic Synapses as Computational Units.
Neural Computation, 1999

Analog Neural Nets with Gaussian or Other Common Noise Distribution Cannot Recognize Arbitrary Regular Languages.
Neural Computation, 1999

On the Complexity of Learning for Spiking Neurons with Temporal Coding.
Inf. Comput., 1999

On Computations with Pulses.
Inf. Comput., 1999

Neural Computation with Winner-Take-All as the Only Nonlinear Operation.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Fast analog computation in networks of spiking neurons using unreliable synapses.
Proceedings of the ESANN 1999, 1999

1998
On the Effect of Analog Noise in Discrete-Time Analog Computations.
Neural Computation, 1998

Efficient Learning With Virtual Threshold Gates.
Inf. Comput., 1998

A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Spiking Neurons.
Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), 1998

On the Role of Time and Space in Neural Computation.
Proceedings of the Mathematical Foundations of Computer Science 1998, 1998

Models for Fast Analog Computation with Spiking Neurons.
Proceedings of the Fifth International Conference on Neural Information Processing, 1998

1997
Bounds for the Computational Power and Learning Complexity of Analog Neural Nets.
SIAM J. Comput., 1997

Networks of spiking neurons: The third generation of neural network models.
Neural Networks, 1997

Fast Sigmoidal Networks via Spiking Neurons.
Neural Computation, 1997

Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages
Electronic Colloquium on Computational Complexity (ECCC), 1997

On the Effect of Analog Noise in Discrete-Time Analog Computations
Electronic Colloquium on Computational Complexity (ECCC), 1997

On the Complexity of Learning for Spiking Neurons with Temporal Coding
Electronic Colloquium on Computational Complexity (ECCC), 1997

Dynamic Stochastic Synapses as Computational Units.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

On the Complexity of Learning for a Spiking Neuron (Extended Abstract).
Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997

On the Relevance of Time in Neural Computation and Learning.
Proceedings of the Algorithmic Learning Theory, 8th International Conference, 1997

1996
Computing the Maximum Bichromatic Discrepancy with Applications to Computer Graphics and Machine Learning.
J. Comput. Syst. Sci., 1996

Networks of Spiking Neurons: The Third Generation of Neural Network Models
Electronic Colloquium on Computational Complexity (ECCC), 1996

The Computational Power of Spiking Neurons Depends on the Shape of the Postsynaptic Potentials
Electronic Colloquium on Computational Complexity (ECCC), 1996

On the Effect of Analog Noise in Discrete-Time Analog Computations.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract).
Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996

1995
Fast Identification of Geometric Objects with Membership Queries
Inf. Comput., April, 1995

On the Complexity of Function Learning.
Machine Learning, 1995

Editor's Foreword.
J. Comput. Syst. Sci., 1995

On the Computational Power of Noisy Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Efficient Learning with Virtual Threshold Gates.
Proceedings of the Machine Learning, 1995

Theory and Applications of Agnostic PAC-Learning with Small Decision Trees.
Proceedings of the Machine Learning, 1995

1994
Neural Nets with Superlinear VC-Dimension.
Neural Computation, 1994

Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts.
Machine Learning, 1994

On-Line Learning of Rectangles and Unions of Rectangles.
Machine Learning, 1994

Computing the Maximum Bichromatic Discrepancy, with applications to Computer Graphics and Machine Learning
Electronic Colloquium on Computational Complexity (ECCC), 1994

Agnostic PAC-Learning of Functions on Analog Neural Nets
Electronic Colloquium on Computational Complexity (ECCC), 1994

Lower Bounds for the Computational Power of Networks of Spiking Neurons
Electronic Colloquium on Computational Complexity (ECCC), 1994

Neural Nets with Superlinear VC-Dimension
Electronic Colloquium on Computational Complexity (ECCC), 1994

Bounds for the Computational Power and Learning Complexity of Analog Neural Nets
Electronic Colloquium on Computational Complexity (ECCC), 1994

On the Computational Complexity of Networks of Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Efficient Agnostic PAC-Learning with Simple Hypothesis.
Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, 1994

1993
The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape.
Theor. Comput. Sci., 1993

Threshold Circuits of Bounded Depth.
J. Comput. Syst. Sci., 1993

Two Tapes Versus One for Off-Line Turing Machines.
Computational Complexity, 1993

Bounds for the computational power and learning complexity of analog neural nets.
Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, 1993

Agnostic PAC-Learning of Functions on Analog Neural Nets.
Proceedings of the Advances in Neural Information Processing Systems 6, 1993

On the Complexity of Function Learning.
Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993

1992
Lower Bound Methods and Separation Results for On-Line Learning Models.
Machine Learning, 1992

The Complexity Types of Computable Sets.
J. Comput. Syst. Sci., 1992

On-line Learning of Rectangles.
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992

A Solution of the Credit Assignment Problem in the Case of Learning Rectangles (Abstract).
Proceedings of the Analogical and Inductive Inference, 1992

1991
The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines.
Theor. Comput. Sci., 1991

On the Computational Power of Sigmoid versus Boolean Threshold Circuits
Proceedings of the 32nd Annual Symposium on Foundations of Computer Science, 1991

On-Line Learning with an Oblivious Environment and the Power of Randomization.
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991

Fast Identification of Geometric Objects with Membership Queries.
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991

1990
Efficient Design of Boltzmann Machines.
Proceedings of the Advances in Neural Information Processing Systems 3, 1990

On the Complexity of Learning from Counterexamples and Membership Queries
Proceedings of the 31st Annual Symposium on Foundations of Computer Science, 1990

On the Complexity of Learning from Counterexamples and Membership Queries (abstract).
Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

1989
On the Complexity of Learning From Counterexamples (Extended Abstract)
Proceedings of the 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, North Carolina, USA, 30 October, 1989

Extensional Properties of Sets of Time Bounded Complexity (Extended Abstract).
Proceedings of the Fundamentals of Computation Theory, 1989

The Complexity Types of Computable Sets.
Proceedings of the Proceedings: Fourth Annual Structure in Complexity Theory Conference, 1989

1988
On the Use of Inaccessible Numbers and Order Indiscernibles in Lower Bound Arguments for Random Access Machines.
J. Symb. Log., 1988

Lower Bound Arguments with "Inaccessible" Numbers.
J. Comput. Syst. Sci., 1988

Meanders and Their Applications in Lower Bounds Arguments.
J. Comput. Syst. Sci., 1988

Motion Planning Among Time Dependent Obstacles.
Acta Inf., 1988

On the Communication Complexity of Graph Properties
Proceedings of the 20th Annual ACM Symposium on Theory of Computing, 1988

The Complexity of Matrix Transposition on One-Tape Off-Line Turing Machines with Output Tape.
Proceedings of the Automata, Languages and Programming, 15th International Colloquium, 1988

1987
Speed-Up of Turing Machines with One Work Tape and a Two-Way Input Tape.
SIAM J. Comput., 1987

Fast Approximation Algorithms for a Nonconvex Covering Problem.
J. Algorithms, 1987

Two Tapes Are Better than One for Off-Line Turing Machines
Proceedings of the 19th Annual ACM Symposium on Theory of Computing, 1987

Threshold circuits of bounded depth
Proceedings of the 28th Annual Symposium on Foundations of Computer Science, 1987

1986
On the Complexity of Nonconvex Covering.
SIAM J. Comput., 1986

Meanders, Ramsey Theory and Lower Bounds for Branching Programs
Proceedings of the 27th Annual Symposium on Foundations of Computer Science, 1986

An Optimal Lower Bound for Turing Machines with One Work Tape and a Two- way Input Tape.
Proceedings of the Structure in Complexity Theory, 1986

two Lower Bound Arguments with "Inaccessible" Numbers.
Proceedings of the Structure in Complexity Theory, 1986

1985
Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI
J. ACM, January, 1985

Variations on Promptly Simple Sets.
J. Symb. Log., 1985

1984
On the Orbits of Hyperhypersimple Sets.
J. Symb. Log., 1984

Quadratic Lower Bounds for Deterministic and Nondeterministic One-Tape Turing Machines (Extended Abstract)
Proceedings of the 16th Annual ACM Symposium on Theory of Computing, April 30, 1984

Approximation Schemes for Covering and Packing Problems in Robotics and VLSI.
Proceedings of the STACS 84, 1984

1983
Oracle-Dependent Properties of the Lattice of NP Sets.
Theor. Comput. Sci., 1983

1982
Recursively Enumerable Generic Sets.
J. Symb. Log., 1982

1978
The Uniform Regular Set Theorem in a-Recursion Theory.
J. Symb. Log., 1978

1977
On minimal pairs and minimal degrees in higher recursion theory.
Arch. Math. Log., 1977

Eine Funktionalinterpretation der prädikativen Analysis.
Arch. Math. Log., 1977


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