Learning divisive normalization in primary visual cortex
Fig 1
Overview of our divisive normalization (DN) model.
The model takes as input an image covering 1.1° of visual field and predicts neurons’ spike counts in response to this image (details in Fig 2). The model is split into two parts: a core that computes a shared nonlinear feature space and a readout that maps the shared feature space individually to each neuron’s spike count. A. Divisive normalization mechanism (simplified). The visual input is convolved with 32 filters covering 0.4° of visual field and then rectified and exponentiated to produce an excitatory output. The output of each filter is then divided by a weighted sum of the excitatory outputs of all filters with normalization weights pkl and a semi-saturation constant σl. In our general formulation, all weights and constants are learned from the data. B. Readout that maps the shared feature space to each neuron’s spike count through an individual weighted sum over the entire shared feature space and a pointwise output nonlinearity. The readout weights are factorized into a feature vector—capturing the nonlinear feature(s) that a neuron computes—and a spatial mask—localizing each neuron’s receptive field (RF).