Nonlinear Hebbian learning as a unifying principle in receptive field formation

TL;DR

This paper demonstrates that various models of sensory receptive field development can be unified under the principle of Nonlinear Hebbian Learning, which explains receptive field shapes across different sensory modalities and modeling approaches.

Contribution

The study unifies diverse receptive field models into a single framework of Nonlinear Hebbian Learning, showing its broad applicability and predictive power across sensory modalities.

Findings

Receptive fields are constrained by input statistics and preprocessing.

Localization of receptive fields does not require overcompleteness or sparse activity.

The framework applies to visual and auditory models, predicting receptive fields a priori.

Abstract

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.