NIPS Proceedingsβ

Wolfgang Maass

26 Papers

  • 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)