Jochen Triesch

An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress

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.

Bats use Dip Echolocation to overcome rhythmic noise

This study shows that echolocating bats adjust their call timing to avoid rhythmic noise, improving perception through adaptive vocal timing.

Hippocampal network activity changes during early epileptogenesis predict subsequent epilepsy

The paper identifies early changes in hippocampal network activity that predict subsequent epilepsy in rats following perforant pathway stimulation.

An energy information trade-off explains hidden structure in the neuronal parameter space

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

Local synaptic competition and global homeostatic regulation of synaptic resources as a unified framework for synaptic scaling

The paper proposes a unified framework combining local synaptic competition and global homeostatic regulation to maintain stable neuronal firing rates despite synaptic modifications.

Hippocampus and Striatum Showed Distinct Contributions to Longitudinal Changes in Value-Based Learning in Middle Childhood

This study shows that hippocampal and striatal volumes in children relate to different aspects of value-based learning over two years, with hippocampus linked to delayed learning and striatum to both immediate and delayed learning.

A developmental increase of inhibition promotes the emergence of hippocampal ripples

This study shows that inhibition matures to promote hippocampal ripple emergence starting from postnatal day 10 in mice.

Biological complexity facilitates tuning of the neuronal parameter space

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.

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

This paper develops a deep learning pipeline for automated dendritic spine detection in 3D live cell imaging data, achieving near-human performance without retraining.

The Development of Active Binocular Vision under Normal and Alternate Rearing Conditions

This paper presents a computational model of active binocular vision development that reproduces experimental findings and predicts new effects under altered rearing conditions.

A phenomenological model of neuroimmune interactions in epileptogenesis

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.

A Computational Model of the Effect ofShort-Term Monocular Deprivation onBinocular Rivalry in the Context of Amblyopia

This paper develops a spiking neural network model to explain how short-term monocular deprivation affects binocular rivalry and suggests inhibitory plasticity is key to amblyopia recovery.

Active Efficient Coding Explains the Development of Binocular Vision and its Failure in Amblyopia

This paper develops a computational model of binocular vision development that explains healthy vision and amblyopia-like states caused by refractive errors.

EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities

The paper uses EEG-triggered TMS to show that motor evoked potentials are more modulated by micro-rhythm phase at weaker stimulation intensities, enabling better differentiation of brain states.

Competition for synaptic building blocks shapes synaptic plasticity

The paper proposes a mathematical model of synaptic competition for neurotransmitter receptors, showing how receptor dynamics influence synaptic plasticity and heterosynaptic plasticity.

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

This paper presents a spiking neural network model showing how synaptic plasticity and small parameter variability lead to lognormal distributions of synaptic efficacies and firing rates, resulting in a neural oligarchy with a few highly influential neurons.

Ongoing brain rhythms shape I-wave properties in a computational model

This paper investigates how ongoing brain rhythms affect TMS-induced I-wave properties in a computational model, finding that phase and power of rhythms significantly influence responses.

Learning of Active Binocular Vision in a Biomechanical Model of the Oculomotor System

The paper presents a model for autonomous learning of active binocular vision using a biomechanical oculomotor system, showing it learns to balance vergence accuracy with metabolic costs.

A Multi-Scale Computational Model of the effects of TMS on Motor Cortex

This paper presents a multi-scale computational model of TMS effects on motor cortex, predicting neuron activation patterns and thresholds based on individual head models.