Spontaneous activity and plasticity in spiking neuronal networks

Understanding how neuronal networks are structured to process sensory inputs is a crucial step to understand the mechanisms underlying the functional role, from an algorithmic point of view, of cerebral activity. In this talk, I will introduce the key modeling concepts allowing to reproduce the fine structure of the neuronal dynamics observed in vivo, using simplified models of spiking neurons, known as ``integrate-and-fire” models. Then, using known facts on synaptic plasticity, I will show how biologically plausible learning rules can, by unsupervised learning, shape the structure of the spontaneous activity, as function of the sensory inputs, and perform useful computational tasks such as pattern classification.