Why neuronal dynamics should control synaptic learning rules

Hebbian learning rules are generally formulated as static rules. Under changing condition (e.g. neuromodulation, input statistics) most rules are sensitive to parameters. In particular, recent work lias focused 011 two different, formulations of spike-t iming-dependent plasticity rules. Additive STT)P [1] is remarkably versatile but also very fragile, whereas multiplicative ST'DP [2. 3] is more robust but lacks attractive features such as synaptic compet it ion and rate stabilization. Here we address the problem of robustness in the additive STDP rule. We derive an adaptive control scheme, where the learning function is under fast dynamic control by postsynaptic activity t o stabilize learning under a variety of conditions. Such a control scheme can be implemented using known biophysical mechanisms of synapses. We show that this adaptive rule makes the additive STDP more robust. Finally, we give an example how meta plasticity of the adaptive rule can be used to guide STDP into different, type of learning regimes.