Natural statistics as inference principles of auditory tuning in biological and artificial midbrain networks

Bats provide a powerful mammalian model to explore the neural representation of complex sounds, as they rely on hearing to survive in their environment. The inferior colliculus (IC) is a central hub of the auditory system that receives converging projections from the ascending pathway and descending inputs from auditory cortex. In this work, we build an artificial neural network to replicate auditory characteristics in IC neurons of the big brown bat. We first test the hypothesis that spectro-temporal tuning of IC neurons is optimized to represent the natural statistics of conspecific vocalizations. We estimate spectro-temporal receptive fields (STRF) of IC neurons and compare tuning characteristics to statistics of bat calls. The results indicate that the FM tuning of IC neurons is matched with the statistics. Then, we investigate this hypothesis on the network optimized to represent natural sound statistics and to compare its output with biological responses. We also estimate biomimetic STRF's from the artificial network and correlate their characteristics to those of biological neurons. Tuning properties of both biological and artificial neurons reveal strong agreement along both spectral and temporal dimensions, and suggest the presence of nonlinearity, sparsity and complexity constraints that underlie the neural representation in the auditory midbrain. Additionally, the artificial neurons replicate IC neural activities in discrimination of social calls, and provide simulated results for a noise robust discrimination. In this way, the biomimetic network allows us to infer the neural mechanisms by which the bat's IC processes natural sounds used to construct the auditory scene. Significance Statement Recent advances in machine learning have led to powerful mathematical mappings of complex data. Applied to brain structures, artificial neural networks can be configured to explore principles underlying neural encoding of complex stimuli. Bats use a rich repertoire of calls to communicate and navigate their world, and the statistics underlying the calls appear to align with tuning selectivity of neurons. We show that artificial neural network with a nonlinear, sparse and deep architecture trained on the statistics of bat communication and echolocation calls results in a close match to neurons from bat's inferior colliculus. This tuning optimized to yield an effective representation of spectro-temporal statistics of bat calls appears to underlie strong selectivity and noise invariance in the inferior colliculus.

Automated summary

Bats provide a powerful mammalian model for exploring the neural representation of complex sounds, and research shows that the spectro-temporal tuning of their inferior colliculus neurons is optimized to match the natural statistics of conspecific vocalizations. The artificial neural network replicates the tuning properties of both biological and artificial neurons, suggesting the presence of nonlinear, sparse, and complex constraints in the neural representation of auditory midbrain. The network also allows for the inference of neural mechanisms underlying the processing of natural sounds in constructing the auditory scene.