no code implementations • 23 May 2024 • Simon Brandt, Mihai Alexandru Petrovici, Walter Senn, Katharina Anna Wilmes, Federico Benitez
In general, we show that adaptation processes at different time scales can cause advanced neuronal responses to time-varying inputs that are modulated on the corresponding time scales.
1 code implementation • 26 Feb 2024 • Laura Kriener, Kristin Völk, Ben von Hünerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
Our resulting model for Efficient Learning of Sequences (ELiSe) builds on these features to acquire and replay complex non-Markovian spatio-temporal patterns using only local, always-on and phase-free synaptic plasticity.
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 • 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.
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 • NeurIPS 2021 • Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai Petrovici
The response time of physical computational elements is finite, and neurons are no exception.
1 code implementation • 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.
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.
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 • 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.
2 code implementations • 24 Dec 2019 • Julian Göltz, Laura Kriener, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Benjamin Cramer, Dominik Dold, Akos Ferenc Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai Alexandru Petrovici
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance.
no code implementations • 15 Nov 2019 • Thomas Mesnard, Gaetan Vignoud, Joao Sacramento, Walter Senn, Yoshua Bengio
This reduced system combines the essential elements to have a working biologically abstracted analogue of backpropagation with a simple formulation and proofs of the associated results.
no code implementations • NeurIPS 2018 • João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience.
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 • 30 Dec 2017 • João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
Animal behaviour depends on learning to associate sensory stimuli with the desired motor command.
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 • 6 Jun 2016 • Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, Joao Sacramento, Walter Senn
We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values.
no code implementations • NeurIPS 2011 • Johanni Brea, Walter Senn, Jean-Pascal Pfister
We consider a statistical framework in which recurrent networks of spiking neurons learn to generate spatio-temporal spike patterns.