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The development of sensory receptive fields has been modeled in the past by avariety of models including normative models such as sparse coding orindependent component analysis and bottom-up models such as spike-timingdependent plasticity or the Bienenstock-Cooper-Munro model of synapticplasticity. Here we show that the above variety of approaches can all beunified into a single common principle, namely Nonlinear Hebbian Learning. WhenNonlinear Hebbian Learning is applied to natural images, receptive field shapeswere strongly constrained by the input statistics and preprocessing, butexhibited only modest variation across different choices of nonlinearities inneuron models or synaptic plasticity rules. Neither overcompleteness nor sparsenetwork activity are necessary for the development of localized receptivefields. The analysis of alternative sensory modalities such as auditory modelsor V2 development lead to the same conclusions. In all examples, receptivefields can be predicted a priori by reformulating an abstract model asnonlinear Hebbian learning. Thus nonlinear Hebbian learning and naturalstatistics can account for many aspects of receptive field formation acrossmodels and sensory modalities.

Nonlinear Hebbian learning as a unifying principle in receptive field formation