Willem A. M. Wybo

The paper introduces a framework for simulating multicompartment neurons with customizable subcellular dynamics using NEST and NESTML, enabling efficient and scalable network simulations.

The paper introduces NEAT, a Python toolkit that implements classical analytical methods to simplify and analyze dendritic computation, enabling integration with simulation tools like NEURON and NEST.

The paper uses simulations to show dendrites sharpen optogenetic responses in cortex, improving spatial fidelity of stimulation.

The paper develops a computational model to explain how context shapes sensory processing by analyzing prediction errors in mice during tactile discrimination learning.

This paper shows dendritic modulation enables multitask representation learning in hierarchical sensory processing by allowing neurons to adaptively extract features across different contexts.

The paper introduces a method to simplify dendritic neuron models while preserving their responses to various spikes, using parameter fits and experimental data without requiring morphological reconstructions.

The paper shows how dendritic compartments can be identified through impedance-based tree-graph analysis and demonstrates that dynamic recompartmentalization enables branch-specific learning.