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Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

Proceedings of the IEEE, 2014-05, Vol.102 (5), p.860-880

Maass, Wolfgang

2014

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Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity

PLoS computational biology, 2013-04, Vol.9 (4), p.e1003037-e1003037

2013

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State-dependent computations: spatiotemporal processing in cortical networks

Nature reviews. Neuroscience, 2009-02, Vol.10 (2), p.113-125

2009

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A solution to the learning dilemma for recurrent networks of spiking neurons

Nature communications, 2020-07, Vol.11 (1), p.3625-3625, Article 3625

2020

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Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

PLoS computational biology, 2011-11, Vol.7 (11), p.e1002211-e1002211

2011

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Network Plasticity as Bayesian Inference

PLoS computational biology, 2015-11, Vol.11 (11), p.e1004485-e1004485

2015

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Edge of chaos and prediction of computational performance for neural circuit models

Neural networks, 2007-04, Vol.20 (3), p.323-334

2007

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Local prediction-learning in high-dimensional spaces enables neural networks to plan

Nature communications, 2024-03, Vol.15 (1), p.2344-2344

2024

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Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning

Cerebral cortex (New York, N.Y. 1991), 2014-03, Vol.24 (3), p.677-690

2014

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Brain computation by assemblies of neurons

Proceedings of the National Academy of Sciences - PNAS, 2020-06, Vol.117 (25), p.14464-14472

2020

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STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning

PLoS computational biology, 2014-03, Vol.10 (3), p.e1003511-e1003511

2014

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Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment

PLoS computational biology, 2014-10, Vol.10 (10), p.e1003859-e1003859

2014

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A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback

PLoS computational biology, 2008-10, Vol.4 (10), p.e1000180-e1000180

2008

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

Neural computation, 2002-11, Vol.14 (11), p.2531-2560

2002

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Neuromorphic Hardware Learns to Learn

Frontiers in neuroscience, 2019-05, Vol.13, p.483-483

2019

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Stochastic computations in cortical microcircuit models

PLoS computational biology, 2013-11, Vol.9 (11), p.e1003311-e1003311

2013

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Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons

PLoS computational biology, 2011-12, Vol.7 (12), p.e1002294-e1002294

2011

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Visualizing a joint future of neuroscience and neuromorphic engineering

Neuron, 2021-02, Vol.109 (4), p.571-575

2021

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On the Computational Power of Winner-Take-All

Neural computation, 2000-11, Vol.12 (11), p.2519-2535

Maass, Wolfgang

2000

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Future Work and Enterprise Systems

Business & information systems engineering, 2018-08, Vol.60 (4), p.357-366

2018

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Elektronische Wissensmärkte: Handel Von Information und Wissen über Digitale Netze

Maass, Wolfgang

1, 2009

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Spike frequency adaptation supports network computations on temporally dispersed information

eLife, 2021-07, Vol.10

2021

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Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

Frontiers in neuroscience, 2018-11, Vol.12, p.840-840

2018

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Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

PLoS computational biology, 2024-03, Vol.20 (3), p.e1011921-e1011921

2024

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A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

eNeuro, 2018-03, Vol.5 (2), p.ENEURO.0301-17.2018

2018

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Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity

State-dependent computations: spatiotemporal processing in cortical networks

A solution to the learning dilemma for recurrent networks of spiking neurons

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

Network Plasticity as Bayesian Inference

Edge of chaos and prediction of computational performance for neural circuit models

Local prediction-learning in high-dimensional spaces enables neural networks to plan

Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning

Brain computation by assemblies of neurons

STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning

Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback

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

Neuromorphic Hardware Learns to Learn

Stochastic computations in cortical microcircuit models

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons

Visualizing a joint future of neuroscience and neuromorphic engineering

On the Computational Power of Winner-Take-All

Future Work and Enterprise Systems

Elektronische Wissensmärkte: Handel Von Information und Wissen über Digitale Netze

Spike frequency adaptation supports network computations on temporally dispersed information

Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

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