The editorial, written by José Roberto Castilho Piqueira and Antonio C. Roque of the University of São Paulo and Renato Teodoro Ramos of the University of Toronto, frames the challenge with unusual clarity. Cognition, they argue, depends on processes occurring across multiple spatial and temporal scales, and theoretical and computational approaches are essential not merely for describing neural activity but for identifying principles that connect these levels of organization and ultimately relate neural dynamics to perception, memory, and behavior. The five studies they highlight range from statistical frameworks for massive electrophysiological recordings to mathematical reviews of differential-equation models and sweeping conceptual proposals about neural communication and emergence. Despite their methodological diversity, all five converge on a single question: how can patterns of neural activity and interaction across scales give rise to organized brain function?

Two of the contributions attack this question head-on using large-scale neural recordings. The first, by Bong and colleagues, addresses a problem that has grown acute as recording technology has advanced: modern experiments can capture oscillatory activity from many brain regions simultaneously, but conventional analysis often reduces these multichannel recordings to single regional signals, discarding the rich structure of interactions between populations. The researchers developed a statistical framework for identifying time-varying relationships between oscillatory activity recorded simultaneously from different brain regions. Rather than collapsing the data, their approach extracts latent components whose amplitude fluctuations reveal inter-regional interactions and their lead-lag structure — that is, which population’s activity tends to precede the other’s in time.

The power of this framework became apparent when the team applied it to local field potentials recorded from prefrontal cortex and visual area V4 during a memory task. The method provided a way of characterizing dynamic communication between high-dimensional neuronal populations, and, crucially, it could identify amplitude-based relationships even in the absence of phase locking or coherence. This is a significant technical advance. Many conventional measures of oscillatory coordination rely on the assumption that interacting brain regions maintain a stable phase relationship, but real neural communication often violates that assumption. By detecting amplitude couplings that other methods miss, the approach complements rather than replaces existing tools, offering a more complete picture of how prefrontal and visual areas talk to each other during working memory.

The second recording-based study, by Olarinre and colleagues, approaches inter-regional coordination from a different angle: transient population events rather than continuous oscillations. Using large-scale Neuropixels recordings — dense silicon probes that can monitor hundreds of neurons across multiple areas at once — from several visual cortical areas and the thalamus, the researchers built a computationally efficient framework for estimating, on individual trials, the timing of stimulus-evoked population bursts and the correlations between those timings across regions. This trial-by-trial perspective matters because averaged activity can hide precisely the variability that carries information about how brain regions coordinate.

Their results revealed both regularities consistent with the known organization of the visual system and substantial animal-to-animal variability. In particular, correlations among cortical areas tended to be stronger than correlations between cortex and thalamus, a finding that aligns with the hierarchical structure of visual processing pathways. Together, the first two studies drive home a point that is reshaping the field: cognition-related neural coordination cannot be understood solely from average activity. Temporal variability, population structure, and dynamically changing relationships between brain regions contain important information that traditional averaging approaches discard.

A third contribution takes a deliberately speculative and integrative stance. Rather than beginning with a particular dataset, Baker and Cariani develop a broad hypothesis about how information might be encoded, distributed, transformed, and communicated in neural systems. Their framework combines traveling waves, temporal spike patterns, phase relationships, synchronization, recurrent networks, and distributed representations with concepts borrowed from wave physics, holography, and communication theory — including the intriguing notion of single sideband communications, a technique from radio engineering that could, in principle, allow neural systems to carry temporally structured information efficiently. In this view, oscillations and traveling waves are not merely correlates of cognition, the way a shadow is a correlate of a body, but potential mechanisms for coordinating distributed neuronal populations and carrying information across the brain. The proposal is deliberately integrative and generates testable hypotheses that can be confronted with increasingly detailed measurements of spatiotemporal neural dynamics — measurements that next-generation recording technologies are just beginning to deliver.

The fourth study tackles a question that is central to all of computational neuroscience: how can descriptions at different scales be connected mathematically? Çetin and colleagues provide a review that organizes differential-equation-based approaches into three interconnected families. Ordinary differential equations provide mechanistic descriptions ranging from single-neuron models — such as the classic Hodgkin-Huxley equations that describe how ion currents generate action potentials — to neural-mass and whole-brain systems that treat entire regions as unified units. Stochastic formulations incorporate the variability that is intrinsic to neuronal activity, acknowledging that real neurons are noisy, not deterministic machines. Mean-field partial differential equations, finally, describe collective dynamics in large neuronal populations, capturing how the distribution of neurons across states evolves over time. Viewed together, these approaches provide mathematical bridges between microscopic neuronal mechanisms and mesoscopic or macroscopic phenomena such as oscillations, synchronization, multistability, and pattern formation. The authors also highlight a critical future challenge: integrating models across scales more systematically and connecting them more tightly with experimental data and modern methods of inference and machine learning.

The fifth contribution broadens the multiscale perspective in a way that challenges a deep assumption of mainstream neuroscience. Oruro and colleagues argue that neuronal morphology — the intricate branching shape of dendrites and axons — should be regarded as a dynamic level of organization in its own right, not simply as a fixed substrate on which neural computation occurs. Their framework connects neuronal form, circuit organization, behavior, and emergent function, drawing examples from development, mother-infant interactions, epilepsy, and neuropsychiatric conditions. This perspective highlights a fundamental feature of brain-based cognition: causal relationships across levels are potentially reciprocal. Cellular and morphological processes shape network dynamics and behavior, while higher-level interactions and experience can, in turn, influence lower-level organization. Experience-dependent changes in dendritic structure, for instance, feed back into the very network dynamics that generated the experience. Computational modeling, the authors contend, offers an important means of investigating these cross-scale relationships that run in both directions simultaneously.

Taken together, the five contributions deliver a message that is both humbling and energizing: there is unlikely to be a single privileged scale or methodology for understanding brain-based cognition. Statistical methods are needed to extract meaningful interactions from increasingly high-dimensional recordings; dynamical models provide mechanistic links between neuronal and population activity; concepts from physics and information processing suggest possible organizing principles for neural communication; and complex-systems approaches emphasize emergence and reciprocal interactions across levels of organization. The challenge ahead, the editors conclude, is to bring these perspectives into closer contact with one another and with experimental data. Progress toward an explanation of cognition will depend not only on increasingly detailed measurements or increasingly sophisticated models, but on the ability to connect structure, dynamics, information, and function across scales. The articles collected here provide complementary steps toward that broader theoretical and computational synthesis — and they suggest that the road to understanding the mind runs not through any single theory, but through the disciplined conversation between them.

Subject of Research: Editorial: Theoretical and computational insights into brain-based cognition

Article Title: Editorial: Theoretical and computational insights into brain-based cognition

Article References: Piqueira, J. R. C., Roque, A. C., & Ramos, R. T. (2026). Editorial: Theoretical and computational insights into brain-based cognition. Frontiers in Computational Neuroscience, 20, Article 1986598. https://doi.org/10.3389/fncom.2026.1986598

Image Credits: AI Generated

DOI: 10.3389/fncom.2026.1986598

Keywords: computational neuroscience, cognition, brain dynamics, neural oscillations, Neuropixels, traveling waves, mean-field models, neural communication, emergence, multiscale modeling, prefrontal cortex, visual cortex