A neural ensemble correlation code for sound category identification

• Published in: PLoS biology 2019-10, Vol.17 (10), p.e3000449-e3000449
• Main Authors: Sadeghi, Mina, Zhai, Xiu, Stevenson, Ian H, Escabí, Monty A
• Format: Article
• Language: English
• Subjects: Acoustic Stimulation - methods; Acoustics; Action Potentials - physiology; Analysis; Animals; Auditory cortex; Auditory discrimination; Auditory perception; Biology and Life Sciences; Biomedical engineering; Categories; Classification; Computer engineering; Correlation; Electrodes, Implanted; Electrophysiology; Female; Hearing; Medicine and Health Sciences; Mesencephalon; Mesencephalon - anatomy & histology; Mesencephalon - physiology; Models, Neurological; Nerve Net - anatomy & histology; Nerve Net - physiology; Neural recording; Neurons; Neurons - cytology; Neurons - physiology; Owls; Pattern Recognition, Physiological - physiology; Physical Sciences; Rabbits; Research and Analysis Methods; Social Sciences; Software; Sound; Stereotaxic Techniques
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3000449

Humans and other animals effortlessly identify natural sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable sound recognition and the formation of perceptual categories are largely unknown. Here, using multichannel neural recordings in the auditory midbrain of unanesthetized female rabbits, we first demonstrate that neural ensemble activity in the auditory midbrain displays highly structured correlations that vary with distinct natural sound stimuli. These stimulus-driven correlations can be used to accurately identify individual sounds using single-response trials, even when the sounds do not differ in their spectral content. Combining neural recordings and an auditory model, we then show how correlations between frequency-organized auditory channels can contribute to discrimination of not just individual sounds but sound categories. For both the model and neural data, spectral and temporal correlations achieved similar categorization performance and appear to contribute equally. Moreover, both the neural and model classifiers achieve their best task performance when they accumulate evidence over a time frame of approximately 1-2 seconds, mirroring human perceptual trends. These results together suggest that time-frequency correlations in sounds may be reflected in the correlations between auditory midbrain ensembles and that these correlations may play an important role in the identification and categorization of natural sounds.