In a groundbreaking study set to redefine our understanding of memory encoding, researchers have unveiled findings that challenge a long-held assumption in neuroscience regarding the temporal ordering of episodic memories. Traditionally, the phase at which neurons fire within ongoing brain oscillations has been thought to encode not just the identity of items in a sequence, but their precise temporal order as well. However, the new research indicates that the firing phase, rather than serving as a linear temporal code, might play a more complex and nuanced role in human sequence memory and related artificial neural networks.
Memory formation and retrieval depend heavily on the brain’s remarkable ability to represent sequences of events, a function critical for everything from language processing to planning complex actions. Previous models have suggested that neurons engaged in sequence encoding showcase their activity in distinct phases of underlying rhythmic brain activity, such as theta or gamma oscillations. These phase relationships were posited to create a neural “timestamp,” marking the position of an event within a string of occurrences. This paradigm has heavily influenced experimental design and computational frameworks in neuroscience and artificial intelligence alike.
The research team, headed by Liebe, Niediek, and Pals, employed a multi-modal approach combining intracranial electrophysiological recordings from human subjects with rigorous computational modeling using recurrent neural networks (RNNs). These human data were acquired during tasks requiring participants to memorize and recall sequences of visual stimuli, enabling unprecedented insight into the neural dynamics underpinning sequence memory. Complementary RNN simulations mirrored these cognitive demands, allowing comparisons between biological and artificial systems under matched conditions.
Their analysis revealed a surprising dissociation: neurons’ firing phase relative to ongoing oscillations did not reliably reflect the serial position of elements within a memorized sequence. Instead, firing phase appeared more tightly associated with other coding dimensions, such as stimulus identity or the detection of salient events within the sequence. Interestingly, recurrent neural networks trained to emulate human sequence memory exhibited similar patterns, with phase-based activity failing to encode temporal order distinctly but rather highlighting identity and saliency features.
This revelation calls into question the conventional interpretation of phase coding as a simple temporal marker in episodic memory circuits. It suggests that brain oscillations might orchestrate sequence memory formation through a multiplexed coding scheme, where timing within oscillatory cycles contributes to richer representational codes beyond linear order. Such multiplexing could provide neural circuits with a flexible framework to interact with diverse cognitive variables, balancing temporal placement, item properties, and context-dependent salience.
The implications extend beyond basic neuroscience, impacting how artificial intelligence systems model human cognition. Recurrent neural networks are pivotal architectures in machine learning, particularly for sequential data processing. Understanding their phase-like activity patterns and their limitations in temporally encoding sequences offers a blueprint for novel mechanistic models. These models would shift away from simplistic temporal tagging toward integrative coding strategies, potentially enhancing memory recall fidelity and sequence prediction in advanced AI.
At the cellular level, intrinsic neuronal properties and synaptic dynamics likely contribute to the observed complex phase relationships. The study highlights how oscillatory timing interacts with synaptic inputs and intrinsic excitability to shape firing patterns that do not strictly follow temporal sequence boundaries. This complexity underscores the need for refined biophysical models that capture how neural populations encode multidimensional information in a temporally dynamic fashion.
Moreover, the interdisciplinary approach blending human neurophysiology with AI modeling exemplifies a new paradigm in cognitive neuroscience. Such integrative methodologies help reconcile discrepancies between animal models, human recordings, and computational theories, offering a holistic understanding of memory mechanisms. The parallel observation of phase coding properties in human neurons and artificial neurons strengthens the validity of these findings and challenges existing theoretical frameworks.
Overall, this study reframes the role of neuronal firing phase in encoding episodic sequences, emphasizing a departure from a unidimensional temporal code toward a participatory coding scheme involving identity and contextual salience. It provokes a reconsideration of how temporal information is embedded into neural circuits and invites further inquiry into the oscillatory dynamics facilitating complex memory tasks.
The authors underscore that while firing phase does not strictly encode temporal order, oscillatory activity still plays a crucial role in orchestrating neural ensemble dynamics, possibly gating information flow or segmenting activity to optimize memory processes. Future research will be needed to elucidate precisely how different oscillation bands and their interactions contribute to multiplexed coding schemes supporting flexible, accurate sequence memory.
This paradigm shift aligns with emerging evidence from other cognitive domains, where timing relative to oscillations intertwines with other neural coding dimensions like amplitude modulation, synchrony, and spike timing plasticity. Together, these mechanisms likely converge to enable the brain’s astounding capacity to represent and manipulate complex event sequences in real time.
The findings pose exciting challenges and opportunities for designing biologically inspired AI systems capable of richer temporal representations. Embedding multiplexed phase coding principles into recurrent architectures may yield breakthroughs in natural language processing, robotics, and memory augmentation technologies.
In closing, this research marks a significant advancement toward unraveling the neural code underlying human sequence memory. By demonstrating that phase of firing does not straightforwardly reflect temporal order, it invites the scientific community to explore new avenues in oscillatory coding and memory research that bridge biology and artificial intelligence.
As our understanding deepens, it becomes increasingly evident that brain oscillations do not merely tick off moments on a mental clock but rather sculpt a dynamic and multi-layered tapestry of neural information, threading together identities, times, and contexts into the cohesive fabric of human experience.
Subject of Research: Sequence memory and neural coding mechanisms in humans and recurrent neural networks.
Article Title: Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks.
Article References:
Liebe, S., Niediek, J., Pals, M. et al. Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks. Nat Neurosci 28, 873–882 (2025). https://doi.org/10.1038/s41593-025-01893-7
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41593-025-01893-7
Tags: artificial neural networks and memorybrain oscillations and memorychallenges to traditional memory modelscomplex role of neuron firingepisodic memory researchimplications for neuroscience researchmemory encodingneural timestamp hypothesisphase firing in neurosciencesequence memory in humanstemporal order in memoriestheta and gamma oscillations
