At the core of this research lies the interaction between slow and fast brain rhythms—namely theta and gamma oscillations—that coordinate neural ensembles during cognitive functions. Traditionally, neuroscientists believed that slow oscillations orchestrate the amplitude modulation of faster rhythms in a unidirectional fashion, effectively gating when and how information is processed. However, this new study reveals a bidirectional relationship: not only do theta waves regulate gamma activity, but gamma rhythms also influence theta oscillations, with this intricate interplay being sculpted by two distinct forms of inhibition—feedforward and feedback inhibition.

Using a unique fusion of computational modeling and electrophysiological recordings, the researchers focused on the hippocampus, a region paramount for memory formation and spatial navigation. Their experimental data, obtained from rats navigating novel and familiar environments, demonstrate that the brain flexibly switches between communication modes depending on context. In familiar settings, feedforward inhibition predominates, promoting gamma-to-theta interactions that prioritize reactivation of stored memories by channeling sensory information directly from the entorhinal cortex to the hippocampus. Conversely, when encountering novelty, feedback inhibition arises, fostering theta-to-gamma coupling that integrates incoming sensory input with memory traces, enabling the updating of stored representations.

This continuous transition between inhibitory modes hinges critically on synaptic strength and connectivity within neural circuits. Unlike a binary switch, the balance between feedforward and feedback inhibition is fluid, allowing the brain to finely tune its processing strategies in real-time to meet cognitive demands. Such flexibility embodies an elegant neural mechanism by which the brain configures its internal communication architectures according to situational exigencies.

“In contrast to the long-held notion that brain rhythms are strictly hierarchical and unilateral in their interactions, our findings uncover a dynamic, bidirectional dance,” explains Dimitrios Chalkiadakis, the study’s first author. “By adjusting inhibitory influences, neural circuits effectively ‘choose’ which information streams to prioritize—whether recalling past experiences or engaging with novel sensory inputs.”

Delving deeper into the mechanistic underpinnings, the computational framework developed by the team simulates the delicate balancing act of inhibitory neurons modulating excitatory pathways. Feedforward inhibition typically targets principal cells soon after they receive input, serving as a rapid gatekeeper, while feedback inhibition arises from the activation of local interneurons that reciprocally regulate those same principal cells. This dual inhibitory architecture orchestrates the directionality of cross-frequency coupling, shaping the theta-gamma code believed to underpin complex cognitive functions.

The implications of this research extend far beyond memory and navigation. Since similar oscillatory interactions also appear in attentional processes, the flexible modulation of inhibitory circuits could represent a fundamental principle governing how the brain allocates computational resources among competing demands. Emerging human neurophysiological data support this view, revealing patterns congruent with the computational insights derived from rodent models.

Furthermore, this study provides a unifying framework reconciling previously conflicting theories regarding the origin and modulation of brain rhythms. Rather than being solely intrinsic to local circuits or inherited from upstream regions, theta and gamma oscillations emerge from an interplay between external inputs and the fine-tuned local inhibitory dynamics, a dual mechanism that heightens the brain’s adaptive prowess.

Looking ahead, the authors aim to extend their models to encompass the immense heterogeneity of neuronal types and architectures that characterize different brain regions, striving for a comprehensive understanding of how inhibitory balance modulates cognition at large. This expanded perspective holds promise for elucidating pathological states as well—disorders like epilepsy, addiction, and Alzheimer’s disease, characterized by dysregulated inhibition and oscillatory abnormality, may benefit from mechanistic insights gained through such studies.

By dissecting the biophysical and computational principles governing inhibitory control over brain rhythms, this research not only deepens fundamental neuroscience but also opens avenues for novel therapeutic strategies. Targeting the precise inhibitory balances that gate information flow could pave the way for interventions to restore cognitive function in neurological and psychiatric conditions.

Bolstered by funding from the Spanish Ministry of Science, Innovation, and Universities and the Spanish State Research Agency, the study exemplifies international, cross-disciplinary collaboration at the interface of physics, computational modeling, and experimental neuroscience. It underscores the power of integrating theory with empirical data to decode the brain’s dynamic language.

In sum, Mirasso, Canals, and colleagues reveal a mesmerizing choreography within the neural substrate, whereby inhibitory circuits flexibly steer the brain’s internal conversation. This discovery heralds a paradigm shift, illuminating how the brain’s rhythmic symphony adapts its flow of information to navigate the demands of memory, novelty, attention, and beyond.

Subject of Research: Not applicable

Article Title: The role of feedforward and feedback inhibition in modulating theta-gamma cross-frequency interactions in neural circuits

News Publication Date: 13-Aug-2025

Web References: http://dx.doi.org/10.1371/journal.pcbi.1013363

References: Chalkiadakis, D., et al. 2025. Instituto de Neurociencias UMH CSIC

Image Credits: Chalkiadakis, D., et al 2025. Instituto de Neurociencias UMH CSIC

Keywords: Brain structure

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