Peter Jedlička

The paper develops an energy-dependent integrate-and-fire model to explain how neurons adapt their biophysical parameters under metabolic stress to maintain an optimal energy-information trade-off.

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.

The paper shows that different ion channel configurations and neuron shapes can produce similar electrophysiological behaviors in CA1 pyramidal neurons, demonstrating robust degeneracy.

The paper shows that neuronal parameter diversity is structured by energy-information trade-offs, forming a Pareto front in parameter space.

This study uses in vitro rMS and computational modeling to show that rTMS enhances synaptic plasticity through cooperative pre- and postsynaptic activity mediated by BDNF.

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 study shows that axon morphology and intrinsic cellular properties influence the intensity needed for rTMS to induce synaptic plasticity in rat versus mouse tissue cultures.

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.

This study investigates the role of amyloid precursor protein (APP) in synaptic transmission at medial perforant path synapses using organotypic cultures and finds that presynaptic APP affects neurotransmission efficacy.

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.

This study shows that all-trans retinoic acid induces synaptopodin-dependent metaplasticity in mouse dentate granule cells without altering baseline synaptic transmission.

The paper introduces NeMo-TMS, a toolbox for simulating TMS-induced neuronal and subcellular responses across scales, enabling detailed modeling of TMS effects.

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.

This paper develops a computational model of neuroimmune interactions in epileptogenesis, showing how injury intensity and stochasticity influence seizure development and revealing multiple pathways to epilepsy.

This study shows all-trans retinoic acid induces synaptic plasticity in human cortical neurons by causing structural and functional changes in excitatory synapses.

This paper shows that glutamatergic inputs influence GABAergic Cl- fluxes, enhancing inhibition in mature neurons and suppressing changes in immature neurons.

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