Search Results for author: Christian Pehle

Found 10 papers, 0 papers with code

Event-based Backpropagation for Analog Neuromorphic Hardware

no code implementations13 Feb 2023 Christian Pehle, Luca Blessing, Elias Arnold, Eric Müller, Johannes Schemmel

Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances.

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

no code implementations26 Jan 2022 Christian Pehle, Sebastian Billaudelle, Benjamin Cramer, Jakob Kaiser, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Aron Leibfried, Eric Müller, Johannes Schemmel

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives.

Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks

no code implementations17 Sep 2020 Timo C. Wunderlich, Christian Pehle

Spiking neural networks combine analog computation with event-based communication using discrete spikes.

Surrogate gradients for analog neuromorphic computing

no code implementations12 Jun 2020 Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke

To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.

Neuromorphic Hardware learns to learn

no code implementations15 Mar 2019 Thomas Bohnstingl, Franz Scherr, Christian Pehle, Karlheinz Meier, Wolfgang Maass

In contrast, the hyperparameters and learning algorithms of networks of neurons in the brain, which they aim to emulate, have been optimized through extensive evolutionary and developmental processes for specific ranges of computing and learning tasks.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

no code implementations8 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.

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