Christoph Miehl

The paper shows that combining different STDP rules generates diverse neural network motifs, but no single rule can produce all possible structures.

The paper proposes a computational model of neuronal assemblies that uses dendritic compartments to prevent catastrophic forgetting and enables flexible learning of new stimuli without disrupting existing connections.

This study shows that combining Hebbian plasticity with homeostatic inhibition allows for stable, tunable neuronal assemblies rather than binary outcomes.

The paper introduces FACED 2.0, a high-speed imaging technique that enables simultaneous voltage and calcium activity recording in vivo across large neuronal populations.

This paper proposes a novel form of nonlinear inhibitory plasticity that stabilizes excitatory synaptic strengths and enables learning through disinhibition.

This study shows inhibitory synaptic plasticity generates novelty responses in a network model by altering inhibition levels based on stimulus familiarity.

The paper shows how SOM-mediated modulation in E-PV-SOM networks can increase neuronal gain and stability simultaneously, depending on connectivity and network state.

This study shows that non-random neural connectivity can emerge through triplet spike timing dependent plasticity without external input or symmetric STDP rules.

The paper shows how heterosynaptic plasticity calibrates excitatory and inhibitory inputs onto pyramidal neurons to achieve excitatory-inhibitory balance.