2026 Wolfgang Maass: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	78	1188	1147	9	8	329	28302

Research.com Recognitions

2026 - Research.com Computer Science in Austria Leader Award
2025 - Research.com Computer Science in Austria Leader Award
2023 - Research.com Computer Science in Austria Leader Award
2022 - Research.com Computer Science in Austria Leader Award
2013 - Member of Academia Europaea

Overview

Wolfgang Maass is affiliated with Graz University of Technology in Austria. Their research spans several fields including Engineering, Neuroscience, and Computer Science, with a particular focus on Electrical and Electronic Engineering, Cognitive Neuroscience, and Artificial Intelligence. Their work often contributes to advanced studies in neural and computational systems.

The primary topics in Maass's research include:

Advanced Memory and Neural Computing
Neural dynamics and brain function
Ferroelectric and Negative Capacitance Devices
Neural Networks and Reservoir Computing
Neural Networks and Applications
CCD and CMOS Imaging Sensors
EEG and Brain-Computer Interfaces

Frequent publication venues for Maass's work include:

bioRxiv (Cold Spring Harbor Laboratory)
arXiv (Cornell University)
Nature Communications
Nature Machine Intelligence
Proceedings of the IEEE

Some of the recent papers published by Maass or closely related to their research collaborations are:

"Embedded Devices for Neuromorphic Time-Series Assessment" (2022), Maryland Shared Open Access Repository (USMAI Consortium)
"A solution to the learning dilemma for recurrent networks of spiking neurons" (2020), Nature Communications
"Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes" (2021), Nature Machine Intelligence
"A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware" (2022), Nature Machine Intelligence
"Brain computation by assemblies of neurons" (2020), Proceedings of the National Academy of Sciences

Maass has collaborated frequently with several researchers in their field, including:

Franz Scherr
Robert Legenstein
Anand Subramoney
Guillaume Bellec
Ceca Kraišniković

In 2013, Wolfgang Maass was recognized as a Member of Academia Europaea.

Best Publications

Real-time computing without stable states: a new framework for neural computation based on perturbations

Wolfgang Maass;Thomas Natschläger;Henry Markram

4681
Citations
Pulsed Neural Networks

Wolfgang Maass;Christopher M. Bishop

1300
Citations
State-dependent computations: spatiotemporal processing in cortical networks

Dean V. Buonomano;Wolfgang Maass

1096
Citations
Approximation schemes for covering and packing problems in image processing and VLSI

Dorit S. Hochbaum;Wolfgang Maass

957
Citations
2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models

Robert Legenstein;Wolfgang Maass

612
Citations
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

Lars Buesing;Johannes Bill;Bernhard Nessler;Wolfgang Maass

510
Citations
A solution to the learning dilemma for recurrent networks of spiking neurons

Guillaume Emmanuel Fernand Bellec;Franz Scherr;Anand Subramoney;Elias Hajek

493
Citations
Threshold circuits of bounded depth

András Hajnal;András Hajnal;Wolfgang Maass;Wolfgang Maass;Pavel Pudlák;Pavel Pudlák;György Turán;György Turán

492
Citations
On the Computational Power of Winner-Take-All

Wolfgang Maass

440
Citations
Threshold circuits of bounded depth

Andras Hajnal;Wolfgang Maass;Pavel Pudlak;Mario Szegedy

414
Citations
On the computational power of circuits of spiking neurons

Wolfgang Maass;Henry Markram

383
Citations
Towards a theoretical foundation for morphological computation with compliant bodies

Helmut Hauser;Auke J. Ijspeert;Rudolf M. Füchslin;Rolf Pfeifer

382
Citations
Lower bounds for the computational power of networks of spiking neurons

Wolfgang Maass

348
Citations
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

Robert A. Legenstein;Dejan Pecevski;Wolfgang Maass

338
Citations
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Bernhard Nessler;Michael Pfeiffer;Michael Pfeiffer;Lars Buesing;Wolfgang Maass

337
Citations
The "Liquid Computer": A Novel Strategy for Real-Time Computing on Time Series

T. Natschläger;W. Maass;H. Markram

301
Citations
Computational aspects of feedback in neural circuits

Wolfgang Maass;Prashant Joshi;Eduardo D. Sontag

299
Citations
Fast sigmoidal networks via spiking neurons

Wolfgang Maass

293
Citations
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

Robert Legenstein;Christian Naeger;Wolfgang Maass

283
Citations
Long short-term memory and Learning-to-learn in networks of spiking neurons

Guillaume Emmanuel Fernand Bellec;Darjan Salaj;Anand Subramoney;Robert Legenstein

217
Citations
Networks of Spiking Neurons: The Third Generation of Neural Network Models

Wolfgang Maass

192
Citations

Frequent Co-Authors

Robert Legenstein Graz University of Technology

Henry Markram École Polytechnique Fédérale de Lausanne

Christos H. Papadimitriou Columbia University

Eduardo D. Sontag Northeastern University

Michael Pfeiffer Bosch Center for Artificial Intelligence

Santosh Vempala Georgia Institute of Technology

Peter Auer University of Leoben

Rodney J. Douglas ETH Zurich

Gerhard Neumann Karlsruhe Institute of Technology

Auke Jan Ijspeert École Polytechnique Fédérale de Lausanne

External Links

Personal website of Wolfgang Maass Google Scholar page

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