NIPS Proceedings
^{β}
Books
Wolfgang Maass
28 Papers
Long short-term memory and Learning-to-learn in networks of spiking neurons
(2018)
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
(2018)
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
(2015)
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
(2009)
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
(2009)
STDP enables spiking neurons to detect hidden causes of their inputs
(2009)
Hebbian Learning of Bayes Optimal Decisions
(2008)
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons
(2007)
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
(2007)
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
(2006)
Temporal dynamics of information content carried by neurons in the primary visual cortex
(2006)
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity
(2005)
Principles of real-time computing with feedback applied to cortical microcircuit models
(2005)
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits
(2004)
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons
(2003)
A Model for Real-Time Computation in Generic Neural Microcircuits
(2002)
Finding the Key to a Synapse
(2000)
Foundations for a Circuit Complexity Theory of Sensory Processing
(2000)
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics
(2000)
Neural Computation with Winner-Take-All as the Only Nonlinear Operation
(1999)
A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions
(1998)
Dynamic Stochastic Synapses as Computational Units
(1997)
Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons
(1996)
On the Effect of Analog Noise in Discrete-Time Analog Computations
(1996)
On the Computational Power of Noisy Spiking Neurons
(1995)
On the Computational Complexity of Networks of Spiking Neurons
(1994)
Agnostic PAC-Learning of Functions on Analog Neural Nets
(1993)
A Method for the Efficient Design of Boltzmann Machines for Classiffication Problems
(1990)