Artificial intelligence insights into hippocampal processing - Wirtshafter, Hannah S.; Wilson, Matthew A. (Frontiers in Computational Neuroscience, 2022)
Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in learning and intelligence. A recent agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world 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, the extrahippocampal system (eHPC) can adjust contextual representations depending on the degree of environmental changes and the significance of environmental cues. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. In this opinion, we argue that muZero functions parallel those of the eHPCs. We show that components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cellular representations occur between similar and distinct contexts can be informed by advances in AI. We explain how these advances provide frameworks 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, can be informed by the mechanisms behind muZero learning. We conclude with ways in which AI agents can aid in illuminating prospective questions about neural functioning, and how they may shed light on potential expected answers.