10 citations · 10 across the 6 of their papers we have counts for
7 papers
Efficient and robust control with spikes that constrain free energy
André Urbano, Pablo Lanillos, Sander Keemink
Animal brains exhibit remarkable efficiency in perception and action, while being robust to both external and internal perturbations. The means by which brains accomplish this rema…
Symbolic Discovery of Stochastic Differential Equations with Genetic Programming
Sigur de Vries, Sander W. Keemink, Marcel A. J. van Gerven
Automated scientific discovery aims to improve scientific understanding through machine learning. A central approach in this field is symbolic regression, which uses genetic progra…
Spiking neurons as predictive controllers of linear systems
Paolo Agliati, André Urbano, Pablo Lanillos +3
Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular uni…
Kozax: Flexible and Scalable Genetic Programming in JAX
Sigur de Vries, Sander W. Keemink, Marcel A. J. van Gerven
Genetic programming is an optimization algorithm inspired by evolution which automatically evolves the structure of interpretable computer programs. The fitness evaluation in genet…
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…
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…
Fitting Stochastic Lattice Models Using Approximate Gradients
Jan Schering, Sander Keemink, Johannes Textor
Stochastic lattice models (sLMs) are computational tools for simulating spatiotemporal dynamics in physics, computational biology, chemistry, ecology, and other fields. Despite the…