Maass, Wolfgang 1949-....
Maass, Wolfgang, 1949 August 21-
Wolfgang Maaß computer scientist
Maass, Wolfgang
VIAF ID: 51005110 (Personal)
Permalink: http://viaf.org/viaf/51005110
Preferred Forms
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Maass
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Wolfgang
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1949-....
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100 1 _ ‡a Maass, Wolfgang
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100 1 _ ‡a Maass, Wolfgang
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100 1 _ ‡a Maass, Wolfgang ‡d (1949- )
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100 1 _ ‡a Maass, Wolfgang ‡d (1949- ).
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100 1 _
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Maass, Wolfgang
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1949-
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100 1 _
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Maass, Wolfgang,
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1949 August 21-
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100 1 _ ‡a Maass, Wolfgang, ‡d 1949 August 21-
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100 1 _
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Maass, Wolfgang,
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1949 August 21-
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100 0 _
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Wolfgang Maaß
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computer scientist
4xx's: Alternate Name Forms (6)
Works
| Title | Sources |
|---|---|
| Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. |
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| Coding and learning of behavioral sequences |
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| Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity |
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| complexity of matrix transposition on one tape off-line turing machines |
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| Computational aspects of feedback in neural circuits |
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| Contributions to α- and β-recursion theory |
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| Diskssion des Fokker-Planck-Formalismus fyr "schwere" teilschen in verdynnten Gasen auf Grund einer expliziten <klassischen> Master Equation |
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| Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition |
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| Distributed fading memory for stimulus properties in the primary visual cortex |
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| A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. |
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| Emergence of optimal decoding of population codes through STDP. |
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| Energy-efficient neural network chips approach human recognition capabilities |
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| Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment |
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| Funktionalinterpretation der prädikativen Analysis |
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| Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons |
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| Learned graphical models for probabilistic planning provide a new class of movement primitives |
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| Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity |
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| A learning rule for very simple universal approximators consisting of a single layer of perceptrons |
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| A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware |
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| Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates |
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| Network Plasticity as Bayesian Inference |
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| Neural computing 07.11. - 11.11.94 |
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| On the computational power of threshold circuits with sparse activity |
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| Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons |
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| Probing real sensory worlds of receivers with unsupervised clustering |
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| Pulsed neural networks, 1998: |
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| A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons |
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| Real-time computing without stable states: a new framework for neural computation based on perturbations |
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| Reward-modulated Hebbian learning of decision making |
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| A solution to the learning dilemma for recurrent networks of spiking neurons |
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| Solving Constraint Satisfaction Problems with Networks of Spiking Neurons |
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| Spiking neurons can learn to solve information bottleneck problems and extract independent components |
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| State-dependent computations: spatiotemporal processing in cortical networks |
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| 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 |
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| STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning |
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| Stochastic computations in cortical microcircuit models |
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| Zur Kunst des formalen Denkens |
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