Divisive Normalization Circuits Faithfully Represent Auditory and Visual Stimuli

Divisive normalization is a canonical neural computation employed for sensory adaptation in vision, olfaction and attention modulation. While Divisive Normalization has been proposed to be an efficient coding algorithm, it remains unclear whether such transformation results in information loss in a dynamic setting. Leveraging a previously proposed general mathematical framework called the Divisive Normalization Processor (DNP), we first show that the DNP circuit describes a wide class of neural circuits as well as phenomenological models including the Linear-Nonlinear cascade model. We then demonstrate both theoretically and computationally that the DNP is an invertible operator that could faithfully represent input information given sufficient output samples.