This talk will start with a presentation of two distinct theoretical approaches to characterize synaptic plasticity. The first approach consists in building a minimal biophysical calcium-based model to describe plasticity at glutamatergic synapses onto pyramidal cells. This model can reproduce successfully a large amount of experimental data in various preparations, and several experimental predictions of the model have been verified. The second approach consists instead in reverse engineering plasticity rules from changes in statistics of neuronal activity with experience. We have applied this approach to recordings from primate ITC and shown that changes in statistics of visual responses with familiarity are consistent with unsupervised Hebbian plasticity.
In the last part of the talk I will describe how specific features of synaptic plasticity rules control the type of dynamics that can be generated at the network level. I will show in particular how a network can exploit heterogeneities in synaptic plasticity rules to flexibly change its dynamics using suitable external inputs.

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