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Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain-Machine Interface
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DOI：10.1002/advs.202520125.png)
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Abstract
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Episodic memory integrates what, where, and when of experience into a coherent autobiographical narrative. Decades of research have identified hippocampal place, time, and concept cells as neural correlates of these components. Yet a major challenge remains: real-life memory encoding occurs in high-dimensional, naturalistic settings, where multimodal sensory, emotional, and cognitive processes intertwine across time and context. Traditional paradigms and analytical tools are insufficient to decode the neural activity underlying such complex experiences. Recent advances in artificial intelligence (AI) offer new means to address this challenge. AI models, such as variational autoencoders and multimodal alignment frameworks, can extract latent representations from neural and behavioral data, capturing the naturalistic structure of memory encoding. Large language models further provide powerful frameworks for interpreting subjective memory reports, linking verbal narratives to memory encoding. When integrated with closed-loop brain–machine interfaces (BMIs) capable of recording from and manipulating large populations of neurons in relevant brain regions, these tools make it possible to address the long-standing questions: how to decode memory codes during naturalistic behaviors and whether these memory codes causally generate memories rather than merely correlate with them. This integrated AI–BMI framework outlines a roadmap from mapping to engineering memory, with implications for Alzheimer's disease, traumatic brain injury, and PTSD.
Keywords:
artificial intelligence
brain-machine interface
episodic memory
hippocampus
multimodal modeling
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