Jonas Ranft

This paper develops an analytical framework showing how nonlinear neural networks decompose tasks into local linear regressions and how this structure relates to neural selectivity and activation regions.

The paper presents a model of motor cortex that explains beta oscillations and traveling waves through spatially-structured connectivity and fluctuating inputs.

The paper investigates axon initial segment alterations in multiple sclerosis, finding increased soma-axon initial segment gap in both active and inactive lesions.

This study shows GlyRs and gephyrin dynamically stabilise each other at inhibitory synapses through receptor-scaffold interactions, with GlyR crosslinking reducing gephyrin exchange but not total synaptic levels.

The paper analyzes spiking E-I neuronal modules with long-range connections, showing how stochasticity affects synchronization and phase waves in beta oscillations.

The paper estimates the lifetime of mesoscopic biological structures using a shot-noise process model, showing agreement with simulations and applying it to postsynaptic scaffold domains.

This paper proposes a computational framework for cerebellar learning using perturbations and validates it with in vitro experiments, contradicting the current consensus on synaptic plasticity.