43 citations · 227 across the 23 of their papers we have counts for
26 papers
Upper bounds on the number of causal pieces in spiking neural networks
Dominik Dold, Dennis Elbrächter, Philipp Petersen
The input space of single-spike neural networks decomposes into causal pieces, and the number of causal pieces has been shown to provide a lower bound on the approximation error fo…
Globular cluster Observations for Luminosity-dispersion Distances (GOLD): Project description and velocity catalogue
K. Fahrion, M. A. Beasley, A. Gvozdenko +10
We introduce and describe the Globular cluster Observations for Luminosity-Dispersion distances (GOLD) project, which has the principal aim of determining independent distances bas…
Equivalence of approximation by networks of single- and multi-spike neurons
Dominik Dold, Philipp Christian Petersen
In a spiking neural network, is it enough for each neuron to spike at most once? In recent work, approximation bounds for spiking neural networks have been derived, quantifying how…
Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs
Zacharia A. Rudge, Dominik Dold, Moritz Fieback +2
Memristors are an emerging technology that enables artificial intelligence (AI) accelerators with high energy efficiency and radiation robustness -- properties that are vital for t…
Guidance and Control Neural Network Acceleration using Memristors
Zacharia A. Rudge, Dario Izzo, Moritz Fieback +3
In recent years, the space community has been exploring the possibilities of Artificial Intelligence (AI), specifically Artificial Neural Networks (ANNs), for a variety of on board…
Reconfiguration of pivoting cube ensembles under local sensing constraints using geometric deep learning
Nadezhda Dobreva, Emmanuel Blazquez, Jai Grover +3
We demonstrate that local sensing is sufficient for effective global reconfiguration of homogeneous pivoting cube modular robots in two dimensions. While cube selection (i.e., whic…
Energy efficiency analysis of Spiking Neural Networks for space applications
Paolo Lunghi, Stefano Silvestrini, Dominik Dold +3
While the exponential growth of the space sector and new operative concepts ask for higher spacecraft autonomy, the development of AI-assisted space systems was so far hindered by…
Causal pieces: analysing and improving spiking neural networks piece by piece
Dominik Dold, Philipp Christian Petersen
We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial ne…
Continuous Design and Reprogramming of Totimorphic Structures for Space Applications
Dominik Dold, Amy Thomas, Nicole Rosi +2
Recently, a class of mechanical lattices with reconfigurable, zero-stiffness structures has been proposed, called Totimorphic lattices. In this work, we introduce a computational f…
Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
A. Martina Neuman, Dominik Dold, Philipp Christian Petersen
We study the learning problem associated with spiking neural networks. Specifically, we focus on spiking neural networks composed of simple spiking neurons having only positive syn…
Scalable Network Emulation on Analog Neuromorphic Hardware
Elias Arnold, Philipp Spilger, Jan V. Straub +4
We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This…
Totimorphic structures for space application
Amy Thomas, Jai Grover, Dario Izzo +1
We propose to use a recently introduced Totimorphic metamaterial for constructing morphable space structures. As a first step to investigate the feasibility of this concept, we pre…
Differentiable graph-structured models for inverse design of lattice materials
Dominik Dold, Derek Aranguren van Egmond
Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances…
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods
Anna Himmelhuber, Dominik Dold, Stephan Grimm +2
Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amou…
Neuro-symbolic computing with spiking neural networks
Dominik Dold, Josep Soler Garrido, Victor Caceres Chian +2
Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way. Thus, they c…
Relational representation learning with spike trains
Dominik Dold
Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and ind…
Evaluating the feasibility of interpretable machine learning for globular cluster detection
Dominik Dold, Katja Fahrion
Extragalactic globular clusters (GCs) are important tracers of galaxy formation and evolution. Obtaining GC catalogues from photometric data involves several steps which will likel…
An energy-based model for neuro-symbolic reasoning on knowledge graphs
Dominik Dold, Josep Soler Garrido
Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated t…
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler +1
Computing latent representations for graph-structured data is an ubiquitous learning task in many industrial and academic applications ranging from molecule synthetization to socia…
Machine learning on knowledge graphs for context-aware security monitoring
Josep Soler Garrido, Dominik Dold, Johannes Frank
Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophisti…
SpikE: spike-based embeddings for multi-relational graph data
Dominik Dold, Josep Soler Garrido
Despite the recent success of reconciling spike-based coding with the error backpropagation algorithm, spiking neural networks are still mostly applied to tasks stemming from senso…
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber +22
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 comp…
Fast and energy-efficient neuromorphic deep learning with first-spike times
Julian Göltz, Laura Kriener, Andreas Baumbach +9
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for sho…
Stochasticity from function -- why the Bayesian brain may need no noise
Dominik Dold, Ilja Bytschok, Akos F. Kungl +6
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 encodi…