{1493} revision 4 modified: 12-12-2019 00:21 gmt

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.
  • Δwxh(g(w Tx))\Delta w \propto x h(g(w^T x)) 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