Changing behavioral state and the impact of correlated variability on neural population coding in auditory cortex

Correlated variability within neural populations, sometimes called noise correlation, substantially impacts the accuracy with which information about sensory stimuli can be extracted from neural activity. Previous studies have shown that changes in behavioral state, reflecting phenomena such as attention and/or arousal, can change correlated variability. However, the degree to which these changes impact neural encoding of sensory information remains poorly understood, particularly in the auditory system.

The meaning of sounds: Acoustic to semantic transformations in human auditory cortex

A bird chirping, a glass breaking, an ambulance passing by. Listening to sounds helps recognizing events and objects, even when they are out of sight, in the dark or behind a wall, for example. In this talk, I will discuss how the human brain transforms acoustic waveforms into meaningful representations of the sources, attempting to link theories, models and data from cognitive psychology, neuroscience and artificial intelligence research.

Perception and neural coding of pitch through the lifespan: Peripheral and cortical considerations

Pitch is a primary perceptual attribute of our auditory world, playing a critical role in music, speech, and the organization of the auditory scene into perceptual objects. It has long been thought that stimulus timing information, conveyed by the auditory nerve, underlies and limits our exquisite sensitivity to differences in frequency, and our ability to detect very small fluctuations or modulations in frequency.

Encoding & decoding language representations in human cortex

Abstract: The meaning, or semantic content, of natural speech is represented in highly specific patterns of brain activity across a large portion of the human cortex. Using recently developed machine learning methods and very large fMRI datasets collected from single subjects, we can construct models that predict brain responses with high accuracy. Interrogating these models enables us to map language selectivity with unprecedented precision, and potentially uncover organizing principles.

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