Christian Tetzlaff

The paper shows that dendritic calcium-mediated action potentials enable single neurons to disentangle mixed inputs by clustering temporally correlated synapses.

PyRID is a Python-based reaction-diffusion simulator for molecular biological systems, capable of simulating reactions and interactions between proteins and substrates.

The paper models a CA3-CA1 network to compare learning dynamics and synaptic stability of AcD and nonAcD cells, finding distinct strategies and stability differences.

This study uses computational modeling to show that protein concentration and binding strength variations control cluster formation in synapses, with simulations matching experimental data on PSD95 localization.

The paper develops a computational model linking synaptic actin dynamics and postsynaptic density size to the synaptic tag hypothesis, explaining experimental findings and predicting nonlinear plasticity accumulation.

The paper develops a mathematical model to explain how calcium dynamics in dendrites can mediate both homosynaptic and heterosynaptic plasticity through input timing and spatial diffusion.

This paper investigates how neural trajectories and their subspaces are dynamically controlled in the central nervous system through mechanisms like gain modulation and clustered inhibition.

This paper introduces an unsupervised learning mechanism that enables neurons to specialize in feature combinations, showing convergence to specific representations through a correlation-based learning rule.

The paper shows that correlated asymmetric connectivity in neural networks generates spatiotemporal activity sequences, while symmetric connectivity produces stationary spatial bumps.

The paper shows that spatially heterogeneous inhibition allows sequential neural activity to project onto task-specific subspaces, enabling fast switching controlled by top-down signals.

The paper uses a neural network model with synaptic tagging and capture to study how memory representations are organized and primed over time, showing that learning order and hub structures influence memory consolidation and spontaneous activation.

This paper applies differential Hebbian learning to active noise reduction, achieving up to -140 dB noise elimination in multi-microphone systems.

The paper proposes that synapses use noise to represent uncertainty about postsynaptic dynamics, showing how this noise enables learning and coordination in neuronal networks.

The paper develops a biophysical model to explain how synaptic changes during early phase LTP are maintained through receptor trafficking and structural changes.

This paper develops a theoretical model of synaptic tagging and capture in recurrent neural networks, showing that it enhances memory recall over time.

This study presents a theoretical model showing how synaptic plasticity and scaling coordinate memory allocation and storage in neural circuits without interference.

The paper uses a computational model to study how distractors affect the recall of learned sequences, identifying two distinct effects of distractors on network dynamics.

The paper develops a biophysical model to link actin dynamics to synaptic spine shape fluctuations, showing that discrete polymerization foci drive membrane changes.

The paper shows that learning noisy environmental stimuli requires two distinct phases of plasticity: one for encoding stimulus organization and another for readaptation to noise levels.

This paper develops a mathematical framework to link synaptic mechanisms with memory organization, showing that three mechanisms enable reliable formation of memory relations.

The paper shows that multi-synapse connections can store information robustly despite synaptic turnover, matching experimental data on dendritic spines in mice.

This paper demonstrates how synaptic plasticity and synaptic scaling self-organize neural networks into computationally powerful assemblies for complex task execution.