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Abstract:
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 has focused on two different formulations
of spike-timing-dependent plasticity rules. Additive STDP [1] is
remarkably versatile but also very fragile, whereas
multiplicative STDP [2, 3] is more robust but lacks attractive
features such as synaptic competition 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
to 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.
References
[1] Song, S., K. Miller, and L. Abbott.
Nature Neuroscience
, 3:919-926, 2000.
[2] Rubin, J., D. Lee, and H. Sompolinsky.
Physical Review Letter
, 86:364-367, 2001.
[3] van Rossum, M., B. G-Q, and G. J. Turrigiano.
Neurosci
, 20:8812-8821, 2000.
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