Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information is represented, how partial cues recover broader associations, how new information is written or updated, and how memory-related states can be perturbed. In biological systems, these questions span synapses, neuronal ensembles, hippocampal-cortical interactions, and plasticity; in LLMs, they span weights, activations, context windows, retrieval systems, and external stores. The comparison is therefore functional and experimental rather than anatomical. Human studies reveal sparse concept responses, temporal binding, rapid association formation, episode-specific coding, and recall-related reactivation, but selective intervention remains limited. Rodent studies provide more selective causal access to learning-related ensembles, whereas human and macaque interventions usually affect broader circuits. LLMs lack lived episodic memory, yet they permit unusually direct and repeatable manipulation of internal states and stored information. We argue that this asymmetry creates a new opportunity. LLMs are not ahead in memory itself, but in experimental access. Their tools may help turn broad questions about retrieval, updating, persistence, reversibility, and unintended effects into sharper biological hypotheses. The productive bridge is to transfer experimental logic, not anatomical parts.
Together, current evidence suggests that WM representations are emergent properties of recurrent, hierarchical neural networks whose dynamics are continuously shaped by context, experience, and behavioral demands, providing a flexible neural architecture for adaptive cognition and conscious thought.
Christos Constantinidis, S. Everling, Stefan Treue et al.· Biological Psychiatry· 1 citation
A recurrent neural network is trained, augmented with an episodic memory buffer, to infer context using Bayesian inference as it continuously makes predictions of upcoming scenes while watching naturalistic movies, suggesting a computational mechanism by which context modulates information maintenance and long-term mem...
Hayoung Song, JeongJun Park, Qi-Hong Lu et al.· 0 citations
The results refine prevailing systems consolidation theories by showing that memory consolidation is a circuit-specific and temporally ordered process, rather than a passive gradual phenomenon, and position the EC as a central and dynamic component of remote memory retrieval alongside the PFC.
Memory is increasingly understood as a reconstructive process, yet many memory paradigms assess only the outcome of retrieval rather than the structure of reconstruction itself. Spatial reconstruction tasks (SRTs) provide a powerful approach for examining reconstructive memory because they require participants to recre...
Taylor K. Brown, Ioannis Valoumas, Michael R. Dulas· Frontiers in cognition· 0 citations
These insights reveal that the consolidated engram requires the targeted engagement of a post-learning engram circuit, rather than a simple reactivation of neurons engaged during initial learning.
M. Kenna, James P. Kesby, Li Xu et al.· bioRxiv· 0 citations
Long-term memory relies on neural reactivation mechanisms that support consolidation and retrieval. However, it remains unknown whether these mechanisms also operate during encoding, the initial stage of memory formation. Using a decoding classifier, we analyzed magnetoencephalography (MEG) data from 67 participants as...
G. Fernández-Rubio, Elvira Brattico, M. Kringelbach et al.· bioRxiv· 0 citations
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