Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus
We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic structures in the spectrogram representation of sound such as localized checkerboard patterns and frequency-modulated excitatory subregions flanked by suppressive sidebands. Moreover, several of these novel features resemble neuronal receptive fields reported in the Inferior Colliculus (IC), as well as auditory thalamus and cortex, and our model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC. To our knowledge, this is the first demonstration that receptive fields of neurons in the ascending mammalian auditory pathway beyond the auditory nerve can be predicted based on coding principles and the statistical properties of recorded sounds. The receptive field of a neuron can be thought of as the stimulus that most strongly causes it to be active. Scientists have long been interested in discovering the underlying principles that determine the structure of receptive fields of cells in the auditory pathway to better understand how our brains process sound. One possible way of predicting these receptive fields is by using a theoretical model such as a sparse coding model. In such a model, each sound is represented by the smallest possible number of active model neurons chosen from a much larger group. A primary question addressed in this study is whether the receptive fields of model neurons optimized for natural sounds will predict receptive fields of actual neurons. Here, we use a sparse coding model on speech data. We find that our model neurons do predict receptive fields of auditory neurons, specifically in the Inferior Colliculus (midbrain) as well as the thalamus and cortex. To our knowledge, this is the first time any theoretical model has been able to predict so many of the diverse receptive fields of the various cell-types in those areas.