Send to

Choose Destination
PLoS Comput Biol. 2016 Sep 30;12(9):e1005070. doi: 10.1371/journal.pcbi.1005070. eCollection 2016 Sep.

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

Author information

School of Computer and Communication Sciences and School of Life Science, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne EPFL, Switzerland.
Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.


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.

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center