about
Distributed fading memory for stimulus properties in the primary visual cortexDistributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral InhibitionReal-time computing without stable states: a new framework for neural computation based on perturbationsA quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons.Probing real sensory worlds of receivers with unsupervised clustering.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.A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.Computational aspects of feedback in neural circuitsA learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedbackMotif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates.Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neuronsProbabilistic inference in general graphical models through sampling in stochastic networks of spiking neuronsBayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticityA learning rule for very simple universal approximators consisting of a single layer of perceptrons.State-dependent computations: spatiotemporal processing in cortical networks.STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learningInput prediction and autonomous movement analysis in recurrent circuits of spiking neurons.Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.Coding and learning of behavioral sequences.Network Plasticity as Bayesian InferenceFading memory and kernel properties of generic cortical microcircuit models.Energy-efficient neural network chips approach human recognition capabilities.Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic PlasticityA spiking neuron as information bottleneck.Stochastic computations in cortical microcircuit models.Learned graphical models for probabilistic planning provide a new class of movement primitives.Emergence of optimal decoding of population codes through STDP.Branch-specific plasticity enables self-organization of nonlinear computation in single neurons.Emergence of dynamic memory traces in cortical microcircuit models through STDP.On the computational power of threshold circuits with sparse activity.Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning.Reward-modulated Hebbian learning of decision making.Belief propagation in networks of spiking neurons.A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models.Spiking neurons can learn to solve information bottleneck problems and extract independent components.A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.
P50
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P50
description
hulumtues
@sq
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
Wolfgang Maass
@ast
Wolfgang Maass
@en
Wolfgang Maass
@es
Wolfgang Maass
@nl
Wolfgang Maass
@sl
type
label
Wolfgang Maass
@ast
Wolfgang Maass
@en
Wolfgang Maass
@es
Wolfgang Maass
@nl
Wolfgang Maass
@sl
prefLabel
Wolfgang Maass
@ast
Wolfgang Maass
@en
Wolfgang Maass
@es
Wolfgang Maass
@nl
Wolfgang Maass
@sl
P106
P21
P2456
m/WolfgangMaass
P31
P496
0000-0002-1178-087X
P5463
Maass_Wolfgang
P569
2000-01-01T00:00:00Z