Nonlinear Hebbian learning as a unifying principle in receptive field formation

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.

Cited by in corpus (10)

• Biologically plausible deep learning -- but how far can we go with shallow networks?
• Towards an integration of deep learning and neuroscience
• Representation Learning using Event-based STDP
• A devil's advocate view on 'self-organized' brain criticality
• 'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
• High-dimensional dynamics in a single-transistor oscillator containing Feynman-Sierpinski resonators: effect of fractal depth and irregularity
• Dictionary Learning with Equiprobable Matching Pursuit
• When the goal is to generate a series of activities: A self-organized simulated robot arm
• Correlation-invariant synaptic plasticity
• Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks