Hermann Cuntz

This paper applies control theory to dendritic structures, showing how morphology and input organization affect resilience and energy cost, with implications for neuronal function.

The paper evaluates a computational model of hippocampal granule cells using information theory, finding they balance energy efficiency and functionality along a Pareto optimal front.

The paper examines dendritic morphology of CA2 and CA1 pyramidal neurons, finding gradual morphological changes along the radial axis that suggest a continuum rather than distinct subtypes.

This study shows that retinal ganglion cell regenerative potential is linked to their developmental maturation timeline, with sON cells showing greater injury-induced regression than tON cells.

The paper develops a multi-scale model to simulate rTMS-induced synaptic plasticity in pyramidal neurons, showing frequency- and location-dependent changes in excitatory synapses.

This paper introduces a morphological repair algorithm inspired by dendritic regeneration to reconstruct incomplete human and non-human neuronal dendrites, restoring both morphology and electrophysiological function.

The paper introduces information-theoretic measures for pattern separation that are robust and reliable, demonstrating their application in dentate granule cells and showing how structural changes affect performance.

The paper shows that the skewed spine size distribution in neurons is not affected by synaptic activity or plasticity and forms early in neuronal development.

The paper shows that neuronal models with more ion channel types have more stable and viable parameter combinations, suggesting complexity aids in achieving target excitability.

The paper uses computational modeling to show that synaptic and ion channel changes, rather than dendritic degeneration alone, explain neuronal hyperexcitability in Alzheimer's disease.

The paper shows that neuronal excitability in response to synaptic inputs is invariant to dendritic length, with voltage responses depending only on dendritic diameter and conductivity.

The paper introduces dendritic normalisation to improve learning in sparsely connected neural networks, showing significant performance gains over existing methods.

The paper investigates dendrite development in Drosophila c1vpda neurons, showing that sequential growth and stochastic retraction balance structural and functional requirements.

The paper develops a computational model to explain dendrite branchlet morphology in Drosophila neurons by analyzing actin-modulatory protein roles and dynamics.

The paper presents a generative model that predicts visual cortex map structures and their properties, showing fixed hypercolumn sizes and constant neuronal numbers per pinwheel.

The paper develops an equation to estimate potential synaptic contacts in cortical neurons and finds an excess of putative connections that enable subcellular-level connectivity.

The paper develops a mathematical model of dendritic growth that optimizes wiring efficiency and space-filling through two complementary strategies.