Neural oscillator network modelling of auditory stream segregation

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Imagine yourself at a dinner party. Five conversations are going on around the table, music hums in the background, cutlery rattles and glasses clink. Caught in a dull discussion about stock options, you're wishing you were with the people over your shoulder reminiscing about skiing in the Alps. No matter where you are, your brain is constantly buzzing in a world of sound. How are we able to choose what to tune in to?Although individual sound sources (e.g. a voice, a humming fridge) change over time some features remain constant, like where they're coming from, whether they are high or low in pitch and how often they repeat. In a situation like the one described above we have some control over what we hear, we can focus on an individual voice or conversation whilst pushing other sounds into the background. There is evidence that the brain uses two strategies to differentiate sound sources: 1) by features (e.g. sources are high or low pitch, sources come from different locations), and2) by timing and rhythm (people talk at different speeds and start/stop stop talking at different times).The brain's sound processing pathways separate sounds by their features, resulting in, for example, different groups of neurons responding to high and low pitch sounds. Ongoing brain rhythms (oscillations in brain activity) can synchronise with specific sound sources in order to track them. Combined together this allows the brain to follow specific sound sources with groups of neurons tracking features and synchronisation of their activity tracking these features over time.Dynamical systems is a field of mathematics describing processes (such as brain rhythms) that change over time. Amongst other types of dynamics, it has helped us understand oscillations and synchronisation across biology, physics, and chemistry to social networks and technological applications. Oscillations in the activity of neurons is important for lots of cognitive functions like making decisions, forming memories and enjoying music. The research here focuses on using mathematical theory about oscillations to develop a computer model of the brain regions involved in processing and segregating sound sources. The current state of the art in computer modelling has focused on the first strategy (separating by features). Here, the focus will be on integrating this with the second strategy, paying specific attention to how the brain uses timing and rhythm to segregate sounds. This is based on the hypothesis that synchronisation of oscillations is crucial for tracking sounds over time. Indeed, when we listen to repetitive sounds (like a simple musical rhythm) neurons in both auditory and motor regions of the brain start to fire in time to the beat, even when we aren't moving ourselves. The research aims to reveal how both of these regions working together enable us simultaneously keep track of both the sound source we're focusing on (stock options) and the one we'd like to really be paying attention to (skiing in the Alps).