Laura Kriener

According to our database, Laura Kriener authored at least 19 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Learning efficient backprojections across cortical hierarchies in real time.

Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici

Nat. Mac. Intell., 2024

The Role of Temporal Hierarchy in Spiking Neural Networks.

Filippo Moro, Pau Vilimelis Aceituno, Laura Kriener, Melika Payvand

CoRR, 2024

DelGrad: Exact gradients in spiking networks for learning transmission delays and weights.

Julian Göltz, Jimmy Weber, Laura Kriener, Peter Lake, Melika Payvand, Mihai A. Petrovici

CoRR, 2024

Backpropagation through space, time, and the brain.

Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici

CoRR, 2024

Order from chaos: Interplay of development and learning in recurrent networks of structured neurons.

Laura Kriener, Kristin Völk, Ben von Hünerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici

CoRR, 2024

2023

Gradient-based methods for spiking physical systems.

Julian Göltz, Sebastian Billaudelle, Laura Kriener, Luca Blessing, Christian Pehle, Eric Müller, Johannes Schemmel, Mihai A. Petrovici

CoRR, 2023

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking.

Jason Yik, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Douwe den Blanken, Petrut Bogdan, Sander M. Bohté, Younes Bouhadjar, Sonia M. Buckley, Gert Cauwenberghs, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Reddy Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Jeremy Forest, Steve B. Furber, Michael Furlong, Aditya Gilra, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Gregor Lenz, Rajit Manohar, Christian Mayr, Konstantinos P. Michmizos, Dylan R. Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayça Özcelikkale, Noah Pacik-Nelson, Priyadarshini Panda, Pao-Sheng Sun, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Catherine D. Schuman, Jae-sun Seo, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Kenneth Michael Stewart, Terrence C. Stewart, Philipp Stratmann, Guangzhi Tang, Jonathan Timcheck, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Biyan Zhou, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

CoRR, 2023

Learning Efficient Backprojections Across Cortical Hierarchies in Real Time.

Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Walter Senn, Mihai A. Petrovici

Proceedings of the Artificial Neural Networks and Machine Learning, 2023

2022

The Yin-Yang dataset.

Laura Kriener, Julian Göltz, Mihai A. Petrovici

Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

2021

Fast and energy-efficient neuromorphic deep learning with first-spike times.

Julian Göltz, Laura Kriener, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Benjamin Cramer, Dominik Dold, Ákos F. Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

Nat. Mach. Intell., 2021

Fast and Energy-efficient Deep Neuromorphic Learning.

Julian Göltz, Laura Kriener, Virginie Sabado, Mihai A. Petrovici

ERCIM News, 2021

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons.

Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici

CoRR, 2021

Latent Equilibrium: Arbitrarily fast computation with arbitrarily slow neurons.

Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Fast and deep neuromorphic learning with first-spike coding.

Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Ákos F. Kungl, Oliver Breitwieser, Karlheinz Meier, Johannes Schemmel, Laura Kriener, Mihai A. Petrovici

Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

2019

Fast and deep neuromorphic learning with time-to-first-spike coding.

Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Dominik Dold, Laura Kriener, Ákos Ferenc Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

CoRR, 2019

2018

A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores.

Syed Ahmed Aamir, Paul Müller, Gerd Kiene, Laura Kriener, Yannik Stradmann, Andreas Grübl, Johannes Schemmel, Karlheinz Meier

IEEE Trans. Biomed. Circuits Syst., 2018

A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores.

Syed Ahmed Aamir, Paul Müller, Gerd Kiene, Laura Kriener, Yannik Stradmann, Johannes Schemmel, Karlheinz Meier

CoRR, 2018

2017

An accelerated analog neuromorphic hardware system emulating NMDA- and calcium-based non-linear dendrites.

Johannes Schemmel, Laura Kriener, Paul Müller, Karlheinz Meier

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

From LIF to AdEx neuron models: Accelerated analog 65 nm CMOS implementation.

Syed Ahmed Aamir, Paul Müller, Laura Kriener, Gerd Kiene, Johannes Schemmel, Karlheinz Meier

Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2017