PMID-27690349 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 neuron’s f-I 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 non-trivial statistical structure, and therefore links receptive field development to optimality principles.
- where h is the hebbian plasticity term, and g is the neurons f-I curve (input-output 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
|