Neuromodulatory-prefrontal interactions in primates

灵长类动物的神经调节-前额叶相互作用

• 批准号: BB/W003392/1
• 负责人: Matthew Rushworth
• 金额: $ 564.39万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW003392%2F1
• 关键词: Neuromodulatory; prefrontal; interactions; primates

We aim to understand how two brain systems cooperate and compete to guide behaviour. One system consists of the ascending neuromodulatory systems (ANS). These comprise groups, called nuclei, of brain cells (neurons) that send projections across the brain to exert broad influences over behaviour. The other brain system is prefrontal and anterior cingulate cortex (PFC/ACC). Whereas ANS are present in all mammals and many other animals, PFC/ACC is uniquely specialized in primates.We hypothesize that ANS represent key features of an organism's environment that guide fundamental aspects of behaviour. For example, one nucleus (the raphe nucleus) may encode how good the environment is on average: does it yield rewards at a high or low rate? If the organism is in a highly rewarding environment then all may be well, but if not, then it may be time to seek a better alternative. Other ANS nuclei may encode the organism's uncertainty about such estimates. If the organism's estimates are very uncertain then the environment may be changing, and the organism needs to seek more information to establish a better estimate of the situation.Like ANS, PFC/ACC also represents information about the environment such as reward richness and uncertainty to guide strategic behaviours. What then enables PFC/ACC to support the sophisticated behaviours we observe in primates? How does it interact with ANS? We will test several ideas. One central idea is that information in PFC/ACC is much richer, or 'high dimensional', than information in ANS. For example, PFC/ACC might hold much more specific information about the value of all choices available in the environment. It may encode relationships between component pieces of information. This could guide behaviour in more sophisticated ways and this would be apparent when we compare PFC/ACC and ANS activity.Several brain activity measurements are required. They are made in a primate called a macaque. One approach measures activity with a magnetic resonance imaging (MRI) scanner. Crucially, this simultaneously tells us about activity in PFC/ACC and ANS so that we can compare them and study their interactions. It also enables links to be drawn with human MRI studies. We will exploit our recently developed protocols for MRI recording of PFC/ACC and ANS while animals engage in a rich repertoire of behaviour. A second approach involves electrodes recording activity from the actual computing units of the brain - the neurons - on the millisecond time scale on which they operate. We will use the latest electrodes to record many tens or even hundreds of neurons simultaneously. This allows us to study rich, 'high dimensional' information encoding in PFC/ACC. Finally, we will use a technique, transcranial ultrasound stimulation (TUS), which transiently disrupts activity in a comparatively non-invasive way. Importantly, this approach establishes how activity in one brain region leads to activity elsewhere and ultimately causes behaviour. We are one of very few research teams in the world that can undertake these studies.We believe that developing such an understanding will become important for artificially intelligent (AI) agents. There are precise, mathematical ways to describe reward information and its optimal use in behavioural guidance. Descriptions resembling observations made in ANS already underpin state-of-the-art AI algorithms that learn behaviour via trial-and-error. These do not require the programmer to explicitly program how the agent should behave, but instead specify the agent's high-level goal: the agent then learns what actions achieve this goal. It may be possible to develop the next generation of AI algorithms to achieve their goals by learning more like primates do; PFC/ACC may allow primates to abstract information away from multiple examples, learn the structure of environments, and perform rapid inferences based on individual observations, in a way that AI agents currently cannot.

我们的目标是了解两个大脑系统如何合作和竞争来指导行为。一个系统由上行神经调节系统（ANS）组成。这些细胞群由称为细胞核的脑细胞（神经元）组成，它们向大脑发送投射，对行为产生广泛的影响。另一个脑系统是前额叶和前扣带皮层（PFC/ACC）。尽管ANS存在于所有哺乳动物和许多其他动物中，但PFC/ACC在灵长类动物中是唯一专门化的。我们假设ANS代表了生物体环境的关键特征，这些特征指导了行为的基本方面。例如，一个核（中缝核）可能编码环境的平均好坏：它产生奖励的速度是高还是低？如果生物体处于一个高度有益的环境中，那么一切都可能很好，但如果不是，那么可能是时候寻找更好的替代品了。其他ANS核可能编码生物体对这种估计的不确定性。如果生物体的估计是非常不确定的，那么环境可能正在发生变化，生物体需要寻求更多的信息来建立对情况的更好估计。像ANS一样，PFC/ACC也代表了有关环境的信息，如奖励丰富度和不确定性，以指导战略行为。那么，是什么使PFC/ACC支持我们在灵长类动物中观察到的复杂行为呢？它如何与ANS相互作用？我们将测试几个想法。一个中心思想是PFC/ACC中的信息比ANS中的信息丰富得多，或者说是“高维”。例如，PFC/ACC可能包含有关环境中所有可用选择的价值的更具体的信息。它可以对组成信息片段之间的关系进行编码。这可能会以更复杂的方式指导行为，当我们比较PFC/ACC和ANS活动时，这一点会很明显。它们是由一种叫猕猴的灵长类动物制造的。一种方法是用磁共振成像（MRI）扫描仪测量活动。至关重要的是，这同时告诉我们PFC/ACC和ANS的活动，以便我们可以比较它们并研究它们的相互作用。它还可以与人类MRI研究建立联系。我们将利用我们最近开发的协议PFC/ACC和ANS的MRI记录，而动物从事丰富的剧目的行为。第二种方法涉及电极记录大脑实际计算单元-神经元-在毫秒时间尺度上的活动。我们将使用最新的电极同时记录数十甚至数百个神经元。这使我们能够研究PFC/ACC中丰富的“高维”信息编码。最后，我们将使用一种技术，经颅超声刺激（TUS），它以相对非侵入性的方式瞬时破坏活动。重要的是，这种方法确定了一个大脑区域的活动如何导致其他区域的活动，并最终导致行为。我们是世界上为数不多的能够进行这些研究的研究团队之一。我们相信，开发这样的理解将对人工智能（AI）代理变得重要。有精确的数学方法来描述奖励信息及其在行为指导中的最佳使用。类似于ANS中观察的描述已经支撑了最先进的AI算法，这些算法通过试错来学习行为。这些不需要程序员显式地编程代理应该如何行为，而是指定代理的高级目标：代理然后学习什么动作实现这个目标。有可能开发下一代人工智能算法，通过更像灵长类动物那样学习来实现目标; PFC/ACC可以让灵长类动物从多个示例中提取信息，学习环境的结构，并根据个体观察进行快速推理，这是人工智能目前无法做到的。

项目成果

Imagining the future self through thought experiments.

通过思想实验想象未来的自己。

• DOI: 10.1016/j.tics.2023.01.005
• 发表时间: 2023
• 期刊: Trends in cognitive sciences
• 影响因子: 19.9
• 作者: Miyamoto K

Learning shapes neural geometry in the prefrontal cortex

学习塑造前额叶皮层的神经几何形状

• DOI: 10.1101/2023.04.24.538054
• 发表时间: 2023
• 作者: Wójcik M