Event:

I will present a simple 2-compartmental neuron model together with an error-driven (and hence non-Hebbian), biologically plausible learning rule for dendritic synapses. The rule seeks to minimize discrepancies between somatic firings and the local dendritic potential that predicts the firing. Prediction errors can arise 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.