Just as the artificial intelligence agent muZero can learn in diverse environments without overgeneralizing, the mammalian extrahippocampal system (eHPCS) exhibits similar capabilities. Both muZero and the eHPCS employ general yet specific functions to adapt to changing contexts while encoding and processing information. By drawing parallels between muZero and the hippocampal system, researchers can gain insights into how the eHPCS guides spatial decision-making, adjusts contextual representations, and undergoes transitions in cell representations. Artificial intelligence agents like muZero offer frameworks for investigating the link between neuronal firing and state changes in the brain. The muZero model also provides testable predictions about replay, remapping, and other functions of the eHPCS. Further studies utilizing AI agents could uncover new knowledge about neural functioning and provide answers to long-standing questions in neuroscience.

Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.

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