A macaque monkey's brain activity will be recorded simultaneously in two different systems—the ancient, broad-acting neuromodulatory nuclei and the primate-specialised prefrontal cortex—to reveal how they cooperate and compete to guide behaviour. This matters because we do not understand how the primate prefrontal cortex, which supports sophisticated decision-making, interacts with the older neuromodulatory systems that encode basic features like whether the environment is rewarding or uncertain. The researchers hypothesise that prefrontal cortex holds much richer, higher-dimensional information—for example, the specific value of every available choice—while neuromodulatory nuclei provide cruder, averaged signals. If successful, the work will map the neural circuitry that enables primates to learn the structure of environments, abstract information from multiple examples, and make rapid inferences from single observations. This is primarily fundamental science, but the researchers explicitly connect it to artificial intelligence: current AI algorithms already use mathematical descriptions of reward that resemble those found in neuromodulatory systems. Understanding how the primate prefrontal cortex achieves its flexible, sample-efficient learning could inspire the next generation of AI agents that learn more like animals do, without requiring exhaustive trial-and-error.
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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.
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