17 citations · 51 across the 31 of their papers we have counts for
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Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer +6
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their…
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines
Jesus Garcia Fernandez, Nasir Ahmad, Marcel van Gerven
Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient…
Vibrational Control of Complex Networks
Yuzhen Qin, Fabio Pasqualetti, Danielle S. Bassett +1
The stability of complex networks, from power grids to biological systems, is crucial for their proper functioning. It is thus important to control such systems to maintain or rest…
Discovering Continuous-Time Memory-Based Symbolic Policies using Genetic Programming
Sigur de Vries, Sander Keemink, Marcel van Gerven
Artificial intelligence techniques are increasingly being applied to solve control problems, but often rely on black-box methods without transparent output generation. To improve t…
Subspace Node Pruning
Joshua Offergeld, Marcel van Gerven, Nasir Ahmad
Improving the efficiency of neural network inference is undeniably important in a time where commercial use of AI models increases daily. Node pruning is the art of removing comput…
Gradient-Free Training of Recurrent Neural Networks using Random Perturbations
Jesus Garcia Fernandez, Sander Keemink, Marcel van Gerven
Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their tra…
Efficient Deep Learning with Decorrelated Backpropagation
Sander Dalm, Joshua Offergeld, Nasir Ahmad +1
The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a signific…
Analytical Characterization of Epileptic Dynamics in a Bistable System
Yuzhen Qin, Ahmed El-Gazzar, Danielle S. Bassett +2
Epilepsy is one of the most common neurological disorders globally, affecting millions of individuals. Despite significant advancements, the precise mechanisms underlying this cond…
Universal Differential Equations as a Common Modeling Language for Neuroscience
Ahmed ElGazzar, Marcel van Gerven
The unprecedented availability of large-scale datasets in neuroscience has spurred the exploration of artificial deep neural networks (DNNs) both as empirical tools and as models o…