Wolfgang Maass

German Research Centre for Artificial Intelligence — Germany

Neuroscience · Life Sciences

61h-index19.7kcitations359works5.72yr avg
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Research Topics

Neural dynamics and brain function(178), Advanced Memory and Neural Computing(167), Neural Networks and Applications(118), Neural Networks and Reservoir Computing(51), Machine Learning and Algorithms(41)

Publications359 total

SurvC2 -Comparing question answering strategies for cancer surveillance FAIR tooling
2026Open Access
Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
ArXiv.org·2026Open Access
Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
arXiv (Cornell University)·2026Open Access
DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection
arXiv (Cornell University)·2026Open Access
DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection
ArXiv.org·2026Open Access
Episodic memories make goal directed action selection context-aware and explainable
2025Open Access
Behavioral Time Scale Synaptic Plasticity (BTSP) endows binding of distributed representations with flexible retrieval options
2025Open Access
Neural sampling from cognitive maps enables goal-directed imagination and planning
2025Open Access
A surprising link between cognitive maps, successor-relation based reinforcement learning, and BTSP
2025· 1 citedOpen Access
Neurons have an inherent capability to learn order relations: A theoretical foundation that explains numerous experimental data
2025Open Access
Author Correction: A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning
Nature Communications·2025Open Access
A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning
Nature Communications·2025· 22 citedOpen Access
Software Development and Modeling in the Age of Artificial Intelligence Manifesto
SSRN Electronic Journal·2025· 1 citedOpen Access
Corrections to “Brain-Inspired Computing: A Systematic Survey and Future Trends”
Proceedings of the IEEE·2024
Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy
2024Open Access
Brain-Inspired Computing: A Systematic Survey and Future Trends
Proceedings of the IEEE·2024· 57 cited
Fast learning without synaptic plasticity in spiking neural networks
Scientific Reports·2024· 9 citedOpen Access
Local prediction-learning in high-dimensional spaces enables neural networks to plan
Nature Communications·2024· 13 citedOpen Access
Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations
PLoS Computational Biology·2024· 3 citedOpen Access
How can neuromorphic hardware attain brain-like functional capabilities?
National Science Review·2023· 2 citedOpen Access
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Frequent Co-authors

Robert Legenstein(11), Henry Markram(5), Thomas Natschläger(4), Bernhard Nessler(4), Guillaume Bellec(3), Lars Buesing(3), David Kappel(3), Anand Subramoney(2), Darjan Salaj(2), Helmut Häuser(2), Auke Jan Ijspeert(2), Rudolf Marcel Füchslin(2), Rolf Pfeifer(2), Michael Pfeiffer(2), Dejan Pecevski(2), A. Hajnal(2), Pavel Pudlák(2), Márió Szegedy(2), György Turán(2), Peter Auer(2)