Mechanisms of noise robust representation of speech in primary auditory cortex

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 18; pp. 6792 - 6797
• Main Authors: Mesgarani, Nima, David, Stephen V., Fritz, Jonathan B., Shamma, Shihab A.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 06.05.2014; National Acad Sciences
• Subjects: Acoustic Stimulation; Animals; Auditory cortex; Auditory Cortex - physiology; Biological Sciences; Ears & hearing; Female; Ferrets - physiology; Humans; Modeling; Models, Neurological; Mustela putorius furo; Neurons; Neurons - physiology; Neuropsychology; Noise; Noise reduction; Nonlinear Dynamics; Phonemes; Pink noise; Reverberation; Sensory perception; Signal noise; Signal transduction; Spectrograms; Speech; Speech Perception - physiology; Vocalization, Animal; White noise; hearing; population code; phonemes; cortical
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1318017111

Humans and animals can reliably perceive behaviorally relevant sounds in noisy and reverberant environments, yet the neural mechanisms behind this phenomenon are largely unknown. To understand how neural circuits represent degraded auditory stimuli with additive and reverberant distortions, we compared single-neuron responses in ferret primary auditory cortex to speech and vocalizations in four conditions: clean, additive white and pink (1/f) noise, and reverberation. Despite substantial distortion, responses of neurons to the vocalization signal remained stable, maintaining the same statistical distribution in all conditions. Stimulus spectrograms reconstructed from population responses to the distorted stimuli resembled more the original clean than the distorted signals. To explore mechanisms contributing to this robustness, we simulated neural responses using several spectrotemporal receptive field models that incorporated either a static nonlinearity or subtractive synaptic depression and multiplicative gain normalization. The static model failed to suppress the distortions. A dynamic model incorporating feedforward synaptic depression could account for the reduction of additive noise, but only the combined model with feedback gain normalization was able to predict the effects across both additive and reverberant conditions. Thus, both mechanisms can contribute to the abilities of humans and animals to extract relevant sounds in diverse noisy environments.