1k citations · 2.1k across the 17 of their papers we have counts for
21 papers
Learning Commute-Time-Preserving World Models for Planning
Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch +1
World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning can benefit from latent rep…
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance
Fabian A. Mikulasch, Friedemann Zenke
Self-supervised learning (SSL) by predicting in latent space, without generating the input data itself, learns highly abstract, useful representations. Intuitively, this success is…
Understanding Self-Supervised Learning via Latent Distribution Matching
Fabian A Mikulasch, Friedemann Zenke
Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework that explains the diverse exis…
Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Michael Hauri, Friedemann Zenke
Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract representations for effective planning…
Teaching signal synchronization in deep neural networks with prospective neurons
Nicolas Zucchet, Qianqian Feng, Axel Laborieux +3
Working memory requires the brain to maintain information from the recent past to guide ongoing behavior. Neurons can contribute to this capacity by slowly integrating their inputs…
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
Tengjun Liu, Julia Gygax, Julian Rossbroich +3
Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection…
Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps
Mattias Nilsson, Riccardo Miccini, Clément Laroche +2
As speech processing systems in mobile and edge devices become more commonplace, the demand for unintrusive speech quality monitoring increases. Deep learning methods provide high-…
Theories of synaptic memory consolidation and intelligent plasticity for continual learning
Friedemann Zenke, Axel Laborieux
Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex inter…
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
Julia Gygax, Friedemann Zenke
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nat…
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
Julian Rossbroich, Friedemann Zenke
How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience. Various studies have suggested plausible solutions for back-propagating…
Implicit variance regularization in non-contrastive SSL
Manu Srinath Halvagal, Axel Laborieux, Friedemann Zenke
Non-contrastive SSL methods like BYOL and SimSiam rely on asymmetric predictor networks to avoid representational collapse without negative samples. Yet, how predictor networks fac…
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef +8
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstan…
Brain-Inspired Learning on Neuromorphic Substrates
Friedemann Zenke, Emre O. Neftci
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solv…
Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu, Friedemann Zenke
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial. The requirements of real biological neural networks are ra…
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel +1
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these network…
Surrogate Gradient Learning in Spiking Neural Networks
Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neu…
SuperSpike: Supervised learning in multi-layer spiking neural networks
Friedemann Zenke, Surya Ganguli
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spik…
Continual Learning Through Synaptic Intelligence
Friedemann Zenke, Ben Poole, Surya Ganguli
While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark co…
Experimental constraints on the -nucleus real potential
S. Friedrich, K. Makonyi, V. Metag +61
In a search for mesic states, the production of -mesons in coincidence with forward going protons has been studied in photon induced reactions on C for incident photo…
Determination of the η'-nucleus optical potential
M. Nanova, V. Metag, E. Ya. Paryev +59
The excitation function and momentum distribution of mesons have been measured in photon induced reactions on C in the energy range of 1250-2600 MeV. The experi…
The PANDA GEM-based TPC Prototype
L. Fabbietti, H. Angerer, R. Beck +37
We report on the development of a GEM-based TPC prototype for the PANDA experiment. The design and requirements of this device will be illustrated, with particular emphasis on the…