Michael Schmuker

This paper introduces a spiking point neuron model that decodes slope and amplitude of continuous signals into spikes, showing efficiency and accuracy comparable to detailed biophysical models.

The paper maps odorant sensitivities in the mouse olfactory bulb, revealing a sparse and structured representation of olfactory chemical space through glomerular tuning properties.

This paper develops a spiking network model to study activity-dependent growth rules in neuronal networks after peripheral lesions, finding that excitatory and inhibitory synaptic elements require opposite growth rules for successful rewiring.

The paper explores whether inhibitory plasticity can regulate neural sparseness instead of homeostasis, using a hybrid network with a somato-dendritic learning rule.

The paper identifies molecular receptive ranges of olfactory glomeruli using machine learning and physiological measurements, revealing a chemical map underlying their arrangement.