Neural oscillator network modelling of auditory stream segregation

想象自己在一个晚宴上。五个谈话围绕着桌子进行，背景音乐嗡嗡作响，餐具嘎嘎作响，玻璃杯叮当作响。当你陷入一场关于股票期权的无聊讨论时，你会希望你和那些在你肩膀上回忆在阿尔卑斯山滑雪的人在一起。无论你身在何处，你的大脑都在声音的世界中不断嗡嗡作响。我们如何能够选择调谐到什么？虽然单个声源（例如声音、嗡嗡作响的冰箱）会随着时间的推移而变化，但有些特征仍然保持不变，比如它们来自哪里，音调是高还是低，以及它们重复的频率。在像上面描述的情况下，我们可以控制我们听到的东西，我们可以专注于一个人的声音或对话，同时将其他声音推到背景中。有证据表明，大脑使用两种策略来区分声源：（1）特征（例如，源是高音调或低音调，源来自不同的位置），以及2）通过定时和节奏（人们以不同的速度说话，并在不同的时间开始/停止说话）。大脑的声音处理途径通过声音的特征将声音分开，例如，不同的神经元群对高低音调的声音作出反应。正在进行的大脑节律（大脑活动的振荡）可以与特定的声源同步，以便跟踪它们。结合在一起，这使得大脑能够跟随特定的声源，神经元群跟踪特征，并随着时间的推移同步跟踪这些特征。动力系统是描述随时间变化的过程（如大脑节奏）的数学领域。在其他类型的动力学中，它帮助我们理解了从生物学、物理学和化学到社交网络和技术应用的振荡和同步。神经元活动中的振荡对许多认知功能都很重要，如决策，形成记忆和欣赏音乐。这项研究的重点是利用振荡的数学理论来开发一个处理和分离声源的大脑区域的计算机模型。计算机建模的当前技术水平集中在第一种策略（按特征分离）。在这里，重点将是将其与第二种策略相结合，特别注意大脑如何使用时间和节奏来分离声音。这是基于这样的假设，即振荡的同步对于随着时间的推移跟踪声音至关重要。事实上，当我们听到重复的声音（比如简单的音乐节奏）时，大脑听觉和运动区域的神经元会随着节拍开始放电，即使我们自己没有移动。这项研究旨在揭示这两个区域如何共同作用，使我们能够同时跟踪我们关注的声源（股票期权）和我们真正想要关注的声源（在阿尔卑斯山滑雪）。

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).