Wolfgang Maass

Full Professor, TU Graz

Joined

December 2017

Names

Wolfgang Maass

Emails

****@igi.tugraz.at (Confirmed)

Personal Links

Career & Education History

Full Professor

TU Graz (tugraz.at)

1991 – Present

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
David Kappel, Robert Legenstein, Stefan Habenschuss, Michael T Hsieh, Wolfgang Maass
- OpenReview Archive Direct Upload
- Readers: Everyone
Fast learning without synaptic plasticity in spiking neural networks
Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, Wolfgang Maass
- Scientific Reports
- Readers: Everyone
Slow processes of neurons enable a biologically plausible approximation to policy gradient
Anand Subramoney, Franz Scherr, Guillaume Bellec, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
- 14 Jun 2021
- OpenReview Archive Direct Upload
- Readers: Everyone
Spike frequency adaptation supports network computations on temporally dispersed information
Darjan Salaj, Anand Subramoney, Ceca Kraisnikovic, Guillaume Bellec, Robert Legenstein, Wolfgang Maass
- 14 Jun 2021
- OpenReview Archive Direct Upload
- Readers: Everyone
Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks
Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, Wolfgang Maass
- 14 Jun 2021
- OpenReview Archive Direct Upload
- Readers: Everyone
A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
- 13 May 2021
- OpenReview Archive Direct Upload
- Readers: Everyone
Dimensionality and flexibility of learning in biological recurrent neural networks
Blake Aaron Richards, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Luke Yuri Prince, Arna Ghosh, Roy Pavel Samuel henha Eyono, Franz Scherr
- 03 Aug 2020 (modified: 05 May 2023)
- CCN 2020 Workshop GAC Submission
- Readers: Everyone

Wolfgang Maass

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning

Fast learning without synaptic plasticity in spiking neural networks

Slow processes of neurons enable a biologically plausible approximation to policy gradient

Spike frequency adaptation supports network computations on temporally dispersed information

Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks

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

Dimensionality and flexibility of learning in biological recurrent neural networks

Co-Authors