no code implementations • 30 Apr 2024 • Julian Göltz, Jimmy Weber, Laura Kriener, Peter Lake, Melika Payvand, Mihai A. Petrovici
To alleviate these issues, we propose an analytical approach for calculating exact loss gradients with respect to both synaptic weights and delays in an event-based fashion.
1 code implementation • 25 Apr 2024 • Yannik Stradmann, Julian Göltz, Mihai A. Petrovici, Johannes Schemmel, Sebastian Billaudelle
With an increasing presence of science throughout all parts of society, there is a rising expectation for researchers to effectively communicate their work and, equally, for teachers to discuss contemporary findings in their classrooms.
no code implementations • 25 Mar 2024 • Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici
In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation.
no code implementations • 26 Feb 2024 • Laura Kriener, Kristin Völk, Ben von Hünerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
By applying a fully local, always-on plasticity rule we are able to learn complex sequences in a recurrent network comprised of two populations.
no code implementations • 31 Dec 2023 • Korbinian Schreiber, Timo Wunderlich, Philipp Spilger, Sebastian Billaudelle, Benjamin Cramer, Yannik Stradmann, Christian Pehle, Eric Müller, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment.
1 code implementation • 27 Sep 2023 • Arno Granier, Mihai A. Petrovici, Walter Senn, Katharina A. Wilmes
Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action and learning.
no code implementations • 29 Aug 2023 • Julian Göltz, Sebastian Billaudelle, Laura Kriener, Luca Blessing, Christian Pehle, Eric Müller, Johannes Schemmel, Mihai A. Petrovici
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.
no code implementations • 3 Aug 2023 • Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan
However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs.
no code implementations • 18 May 2023 • Michele Farisco, Gianluca Baldassarre, Emilio Cartoni, Antonia Leach, Mihai A. Petrovici, Achim Rosemann, Arleen Salles, Bernd Stahl, Sacha J. van Albada
The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
1 code implementation • 20 Dec 2022 • Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici
Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas.
1 code implementation • 28 Jan 2022 • Camille Gontier, Jakob Jordan, Mihai A. Petrovici
This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks.
2 code implementations • NeurIPS 2021 • Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
The response time of physical computational elements is finite, and neurons are no exception.
no code implementations • 30 Sep 2021 • Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, Martin Gärttner
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states.
1 code implementation • 9 Sep 2021 • Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan
We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs).
no code implementations • 27 Apr 2021 • Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici, Walter Senn
We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration.
1 code implementation • 16 Feb 2021 • Laura Kriener, Julian Göltz, Mihai A. Petrovici
The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks.
no code implementations • 8 Feb 2021 • Henrik D. Mettler, Maximilian Schmidt, Walter Senn, Mihai A. Petrovici, Jakob Jordan
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.
no code implementations • 23 Nov 2020 • Elena Kreutzer, Walter M. Senn, Mihai A. Petrovici
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights.
no code implementations • 3 Aug 2020 • Stefanie Czischek, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Lukas Kades, Jan M. Pawlowski, Markus K. Oberthaler, Johannes Schemmel, Mihai A. Petrovici, Thomas Gasenzer, Martin Gärttner
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years.
no code implementations • 19 Jun 2020 • Agnes Korcsak-Gorzo, Michael G. Müller, Andreas Baumbach, Luziwei Leng, Oliver Julien Breitwieser, Sacha J. van Albada, Walter Senn, Karlheinz Meier, Robert Legenstein, Mihai A. Petrovici
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately.
2 code implementations • 28 May 2020 • Jakob Jordan, Maximilian Schmidt, Walter Senn, Mihai A. Petrovici
Continuous adaptation allows survival in an ever-changing world.
Neurons and Cognition
no code implementations • 30 Dec 2019 • Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Akos F. Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Gerd Kiene, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control.
no code implementations • 27 Dec 2019 • Sebastian Billaudelle, Benjamin Cramer, Mihai A. Petrovici, Korbinian Schreiber, David Kappel, Johannes Schemmel, Karlheinz Meier
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations.
no code implementations • 8 Nov 2018 • Timo Wunderlich, Akos F. Kungl, Eric Müller, Andreas Hartel, Yannik Stradmann, Syed Ahmed Aamir, Andreas Grübl, Arthur Heimbrecht, Korbinian Schreiber, David Stöckel, Christian Pehle, Sebastian Billaudelle, Gerd Kiene, Christian Mauch, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency.
no code implementations • 21 Sep 2018 • Dominik Dold, Ilja Bytschok, Akos F. Kungl, Andreas Baumbach, Oliver Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
no code implementations • 6 Jul 2018 • Akos F. Kungl, Sebastian Schmitt, Johann Klähn, Paul Müller, Andreas Baumbach, Dominik Dold, Alexander Kugele, Nico Gürtler, Luziwei Leng, Eric Müller, Christoph Koke, Mitja Kleider, Christian Mauch, Oliver Breitwieser, Maurice Güttler, Dan Husmann, Kai Husmann, Joscha Ilmberger, Andreas Hartel, Vitali Karasenko, Andreas Grübl, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
no code implementations • 24 Sep 2017 • Luziwei Leng, Roman Martel, Oliver Breitwieser, Ilja Bytschok, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.
no code implementations • 17 Mar 2017 • Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn, David Stöckel, Anna Schroeder, Guillaume Bellec, Johannes Bill, Oliver Breitwieser, Ilja Bytschok, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Eric Müller, Paul Müller, Johannes Partzsch, Thomas Pfeil, Stefan Schiefer, Stefan Scholze, Anand Subramoney, Vasilis Thanasoulis, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, René Schüffny, Christian Mayr, Johannes Schemmel, Karlheinz Meier
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
no code implementations • 12 Mar 2017 • Mihai A. Petrovici, Anna Schroeder, Oliver Breitwieser, Andreas Grübl, Johannes Schemmel, Karlheinz Meier
How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferring these computational principles to neuromorphic silicon circuits.
1 code implementation • 6 Mar 2017 • Sebastian Schmitt, Johann Klaehn, Guillaume Bellec, Andreas Gruebl, Maurice Guettler, Andreas Hartel, Stephan Hartmann, Dan Husmann, Kai Husmann, Vitali Karasenko, Mitja Kleider, Christoph Koke, Christian Mauch, Eric Mueller, Paul Mueller, Johannes Partzsch, Mihai A. Petrovici, Stefan Schiefer, Stefan Scholze, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, Christian Mayr, Johannes Schemmel, Karlheinz Meier
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
no code implementations • 23 Oct 2016 • Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro.
no code implementations • 5 Jan 2016 • Mihai A. Petrovici, Ilja Bytschok, Johannes Bill, Johannes Schemmel, Karlheinz Meier
The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods.
no code implementations • 29 Apr 2014 • Mihai A. Petrovici, Bernhard Vogginger, Paul Müller, Oliver Breitwieser, Mikael Lundqvist, Lyle Muller, Matthias Ehrlich, Alain Destexhe, Anders Lansner, René Schüffny, Johannes Schemmel, Karlheinz Meier
Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation.
no code implementations • 13 Nov 2013 • Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference.