Vincent Hakim

The paper develops a quantitative theory to explain learning in echo state networks with stable dynamics and weak feedback, showing that learning can be described by nonlinear interactions of master modes.

The paper uses microfluidics to apply controlled morphogen gradients to human embryonic stem cells, showing that absolute concentration shapes cell identity during differentiation.

The paper shows that excitatory synapses on Purkinje cells develop through a stepwise molecular process involving initial common proteins and later input-specific proteins.

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

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.