Schematic view of plasticity change between neurons during sequence generation.

Statistical learning and synaptic plasticity

Far from being static transmission units, synapses are highly complex elements with dynamics ranging from milliseconds to hours or even days. This complexity becomes even more striking when we consider the variability in synaptic dynamics and synaptic plasticity. Some synapses facilitate, other are depressing. For some synapses long-term plasticity is induced with a given stimulation protocol while others depress with the same protocol. How can we make sense of this huge variability? The goal of this project is to propose a unifying theoretical framework which can (at least partially) explain this tremendous variability. The aim is then to determine to what extent the variability in neuronal types can predict the diversity in synaptic dynamics and plasticity in this computational framework.

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