Learning by the Dendritic Prediction of Somatic Spiking

Urbanczik, Robert; Senn, Walter (2014). Learning by the Dendritic Prediction of Somatic Spiking. Neuron, 81(3), pp. 521-528. Cell Press 10.1016/j.neuron.2013.11.030

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Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and
postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic
synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic
firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which
directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward
signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

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