PMID27690349 Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
 Here we show that the principle of nonlinear Hebbian learning is sufficient for receptive field development under rather general conditions.
 The nonlinearity is defined by the neuronâ€™s fI curve combined with the nonlinearity of the plasticity function. The outcome of such nonlinear learning is equivalent to projection pursuit [18, 19, 20], which focuses on features with nontrivial statistical structure, and therefore links receptive field development to optimality principles.
 $\Delta w \propto x h(g(w^T x))$ where h is the hebbian plasticity term, and g is the neurons fI curve (inputoutput relation), and x is the (sensory) input.
 The relevant property of natural image statistics is that the distribution of features derived from typical localized oriented patterns has high kurtosis [5,6, 39]
 Model is a generalized leaky integrate and fire neuron, with triplet STDP
