Understanding Auditory Spectro-Temporal Receptive Fields and Their Changes with Input Statistics by Efficient Coding Principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed. Author Summary: Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations of the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on the ensemble of input stimuli has usually been examined mechanistically as a possibly complex nonlinear process. We propose that the STRFs and their dependence on the input ensemble can be understood by an efficient coding principle, according to which the responses of the encoding neurons report the maximum amount of information about the sensory input, subject to limits on the neural cost in representing and transmitting information. This proposal is inspired by the success of the same principle in accounting for receptive fields in the early stages of the visual pathway and their adaptation to input statistics. The principle can account for the STRFs that have been observed, and the way they change with sound intensity. Further, it predicts how the STRFs should change with input correlations, an issue that has not been extensively investigated. In sum, our study provides a computational understanding of the neural transformations of auditory inputs, and makes testable predictions for future experiments.