The starting point of the model is a contrast with artificial systems. A computer executes commands given from outside; the brain, by contrast, is driven by continuously generated desires rooted in biological needs and experience-dependent values. Tsubo organizes these drives under an Affective Valuation System, or AVS, which he divides into an innate Proto-AVS present from birth, involving survival circuits such as the hypothalamus, amygdala, and brainstem, and a higher-order AVS built through postnatal learning in regions including the orbitofrontal cortex and hippocampus. These valuation signals, the model argues, do not merely evaluate behavior after the fact; they actively initiate and steer the exploration of contextual information in the dorsolateral prefrontal cortex, where the DCEM is centered.

Technically, the DCEM is conceived as a dynamic contextual memory composed of logical Contextual Elements, or CEs, each implemented by multiple physical cortical columns. As a concrete structural example, the model assumes eight CE Categories, labeled Subject, Verb, Object, Complement, When, Where, Why, and How, each containing roughly 25 CEs, for about 200 logical CEs and roughly 2,000 physical columns throughout the system. Tsubo is careful to stress that these numbers are model assumptions introduced to describe the structure and operation of the DCEM, not established neuroanatomical constants. The contents of each CE are assumed to exist in distributed form as numerous Meaning Groups formed through experience within the parietal and temporal association cortices, which the model treats as functional, not strict anatomical, subdivisions handling perceptual and conceptual information respectively.

The model unfolds in two stages of recurrent loops. In the first stage, Layer 2/3 of the DCEM receives inputs from the association cortices, the AVS, and a thalamic population, while large Layer 5 pyramidal neurons project outputs widely back to the association cortices. This forms recurrent loops, PAC/TAC Layer 2/3 to DCEM Layer 2/3 to DCEM Layer 5 and back, that oscillate in the gamma band. Because Layer 2/3 neurons have inhibitory connections with neighboring columns, the formation of a new loop terminates previously active loops nearby, so the candidate set is continuously replaced as inputs change. The result is a state in which numerous asynchronous gamma-band candidate loops run in parallel, exploring diverse contents that could fill each CE, a picture consistent with experimental work linking gamma activity to transient information representation.

The second stage is where desire does its work. A specialized population of integrative neurons, hypothesized to exist in the mediodorsal thalamic nucleus and termed Contextual Element Decision Neurons, or CEDeNs, monitors the candidates. Each CEDeN receives Layer 5 inputs from the multiple physical columns constituting one logical CE, together with positive and negative valuation signals from the AVS. The initiation condition is expressed as a threshold equation in which the summed Layer 5 input plus positive valuation minus negative valuation, multiplied by a gain factor from the Global Neuromodulatory System, must exceed a threshold. In the asynchronous gamma state, the summed Layer 5 inputs alone are unlikely to reach threshold; only when sufficient positive valuation is added does the CEDeN fire and send synchronized feedback to the columns of that CE.

That synchronized feedback triggers a remarkable local circuit dynamic. When simultaneous input arrives at Layers 2 and 3, Layer 2 pyramidal neurons, which are smaller and have less membrane capacitance, depolarize faster and fire first, driving fast-spiking parvalbumin-positive inhibitory interneurons. This inhibition transiently suppresses the larger Layer 3 neurons, delaying their firing until the inhibitory effect decays and excitation reaccumulates. The Layer 3 delay propagates to Layer 5, so the synchronized Layer 5 outputs have a longer period than the gamma band, producing a coherent beta-band Contextual Element Loop. The beta periodicity, in other words, is not imposed from outside but emerges from the intrinsic temporal structure of the L2, PV-interneuron, L3, L5 sequence within each column, a mechanism the author derives from his previous biophysical studies of dendritic spines and neuronal computation.

Once formed, a beta-band CE Loop is relatively self-sustaining. Because the Layer 5 outputs of the columns are now synchronized, they sum temporally in the CEDeN, making maintenance less dependent on continuous positive valuation. But the loop is not permanent: when new sensory information, memories, behavioral outcomes, or language input shift the AVS evaluation in a negative direction, negative signals rise, the CEDeN’s integrated input falls below threshold, synchronized feedback stops, and the loop disappears. Meanwhile, the thalamic reticular nucleus and Layer 6 circuits gate sensory input to favor information relevant to the current context, and recurrent feedback from DCEM Layer 5 keeps reactivating latent representations, preparing the next candidates in parallel. A conscious state, in this framework, is the simultaneous combination of multiple CE Loops, and conscious state transitions are the formation, maintenance, disappearance, and replacement of individual loops.

The framework extends naturally to memory, language, and learning. Episodic memories reactivated through the hippocampus become part of the gamma candidate pool, and only contextual states stabilized as beta CE Loops for a sufficient period are predicted to proceed to memory consolidation in hippocampal circuits, a testable relationship. Language is positioned not as constituting consciousness but as an output format: the language network, including Broca’s area, serializes the nonverbal CE Loop structure into a temporally ordered linguistic sequence. Learning, meanwhile, is interpreted as the reuse of existing CE structures in new combinations, which the author suggests may underlie the rapid human ability to understand new concepts through metaphor and analogy, with long-term consolidation involving NMDA receptor calcium influx, CaMKII activation, and structural plasticity of dendritic spines.

The model is explicit about how it relates to existing theories of consciousness. The CEDeN threshold mechanism bears functional similarity to the ignition of Global Workspace Theory, but in DCEM it operates separately for each logical CE rather than broadcasting an entire representation globally. Unlike Integrated Information Theory, DCEM does not quantify the integration of consciousness but addresses the temporal state-transition mechanism of contextual selection and replacement. And while it shares with Predictive Processing the property that states update on the basis of external and internal information, DCEM uniquely introduces continuously provided desires and valuation as the driving conditions for context formation, proposing a specific circuit mechanism for how individual CEs are selected, synchronized, and terminated when valuation changes.

Crucially, the model is designed to be falsifiable. It predicts that preserving cortical gamma candidate activity while selectively suppressing the corresponding CEDeN should leave the candidates intact but selectively impair the transition to synchronized beta activity and CE Loop formation, and conversely that selective CEDeN activation should facilitate that transition without disrupting other coexisting candidates. It predicts that beta Loop maintenance should not require continuous positive valuation, that rising negative valuation should selectively terminate the corresponding loop, and that disrupting beta synchronization for one CE should selectively impair decoding of that CE’s information downstream. Testing these predictions will require laminar recordings, simultaneous measurement of the mediodorsal thalamus and cortex, and cell-type-selective manipulations. The author acknowledges that the existence of CEDeNs and several biophysical mechanisms, including membrane-potential formation from retained spine charge, remain hypotheses requiring direct experimental verification. If the principal predictions hold, the DCEM model could provide a new biophysical foundation linking the intrinsic properties of single neurons to the large-scale dynamics through which desire becomes context, and context becomes consciousness.

Subject of Research: A biophysical framework for conscious state transitions based on intrinsic neuronal properties: the dynamic contextual element memory (DCEM) model

Article Title: A biophysical framework for conscious state transitions based on intrinsic neuronal properties: the dynamic contextual element memory (DCEM) model

Article References: Tsubo, T. (2026). A biophysical framework for conscious state transitions based on intrinsic neuronal properties: the dynamic contextual element memory (DCEM) model. Frontiers in Systems Neuroscience, 20, Article 1894336. https://doi.org/10.3389/fnsys.2026.1894336

Image Credits: AI Generated

DOI: 10.3389/fnsys.2026.1894336

Keywords: biophysical, framework, conscious, state, transitions, based, intrinsic, neuronal, properties, dynamic, contextual, element

The core idea builds on a well-established principle in wave physics: parabolic reflectors focus incoming parallel wavefronts to a single point, much as a satellite dish concentrates electromagnetic signals. A single surface-piercing parabolic breakwater can already act as a wave concentrator, bending incoming ocean waves so that their energy converges at a focal region where a wave energy converter could be placed. But the Dalian team went a step further. They coupled this primary parabolic concentrator with a second, fully submerged parabolic structure positioned downstream. This secondary wall scatters the incoming waves with a delay, producing scattered waves that interact with the primary focused field in ways that can either amplify or suppress the overall concentration depending on the relative phases of the two wave paths.

What makes this approach distinctive is that the focusing is no longer determined solely by geometry. Because the secondary element generates delayed scattered waves, the final focal amplification depends on multi-path superposition, the interference between waves that have traveled different routes to reach the same point. When the scattered waves from the submerged wall arrive at the focal region in step with the primary focused field, constructive interference boosts the wave height dramatically. When they arrive out of step, destructive interference can flatten the focus entirely. This gives engineers a phase-based mechanism for tuning wave focusing, something that a single fixed breakwater simply cannot offer.

To explore this mechanism quantitatively, the researchers developed a second-order three-dimensional higher-order boundary element model. Boundary element methods reduce the computational burden of simulating wave-structure interactions by solving for the fluid behavior only on the boundaries of the domain rather than throughout the entire volume. The second-order formulation captures nonlinear wave effects that become important when waves are strongly focused and wave heights grow large relative to their wavelengths. The team verified the mesh convergence of their model numerically, ensuring that refining the computational grid did not change the results, and validated the single-breakwater baseline against wave-basin experiments, giving confidence that the simulations reflect real hydrodynamics.

With this validated tool in hand, the researchers systematically varied the configuration of the dual-breakwater system. One of the most consequential parameters is the distance between the two focal points, one associated with the primary parabolic concentrator and one with the submerged secondary structure. Changing this separation alternates between constructive and destructive interference at the focal region, and the team found that the optimal configuration shifts in a predictable manner as the distance changes. This predictability is crucial for practical design, because it means engineers can compute the required spacing for a given wave climate rather than relying on trial and error.

The chord length of the secondary wall, the horizontal extent of the submerged parabolic element, also plays a surprising role. The team discovered that its effect on focal amplification is non-monotonic, meaning that increasing the chord length does not simply increase or decrease the focus in a straightforward way. Instead, changing the chord length alters the effective phase of the scattered waves produced by the secondary wall, which in turn shifts whether those waves reinforce or oppose the primary focused field. A longer wall is not necessarily better; there is an optimal size that depends on the wavelength of the incoming waves and the geometry of the overall system.

These findings establish clear frequency selectivity in the dual-breakwater system. Because the phase relationship between the primary focused waves and the delayed scattered waves depends on the wavelength of the incoming swell, the configuration that produces maximum amplification at one wave frequency will not necessarily do so at another. This is a double-edged sword for wave energy applications. On one hand, frequency selectivity means the system can be tuned to match the dominant wave frequencies at a particular site, maximizing energy concentration for the waves that carry the most power. On the other hand, it means performance will vary across the broad spectrum of wave conditions that any real ocean location experiences, and designers must account for this variability when integrating wave energy converters with the breakwater.

The broader motivation for this work lies in the economics of coastal renewable energy. Wave energy converters remain expensive to deploy and maintain, and one promising pathway to reducing costs is to integrate them with existing or planned coastal infrastructure. A breakwater that also serves as a wave concentrator could share foundation costs, reduce the need for separate mooring and anchoring systems, and improve the use of nearshore wave energy, where waves have already been partially shaped by the coastal bathymetry. The dual-parabolic configuration proposed here takes this integration concept further by adding an active tuning mechanism, allowing the focusing behavior to be adjusted through the geometry of the secondary element rather than requiring a completely new primary structure.

The study was financially supported by the National Natural Science Foundation of China under grants U25B20228, U22A20242, and 52271260. The work was published open access under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, making the detailed findings available to researchers and engineers worldwide. The article was received on 12 March 2026, accepted on 2 September 2026, and published on 14 September 2026, with a transparent peer review file and supplementary information available alongside the main paper.

As coastal communities grapple with rising seas and growing energy demands, the idea that the same structure can protect a shoreline and concentrate wave energy for harvesting represents a compelling convergence of engineering goals. The phase-based tuning mechanism identified by the Dalian and Bath team provides a quantitative foundation for designing such hybrid systems, offering guidance that goes beyond intuition to specific geometric relationships between focal point separation, chord length, and wave frequency. Whether these dual-parabolic breakwaters will move from numerical models and wave basins into real ocean deployments remains to be seen, but the study demonstrates that the physics of wave interference can be harnessed deliberately, not just observed, opening a new dimension in the design of multifunctional coastal structures.

Subject of Research: Phase-tuned wave focusing by dual-parabolic breakwaters with a submerged secondary element

Article Title: Phase-tuned wave focusing by dual-parabolic breakwaters with a submerged secondary element

Article References: Zhang, X., Ning, D., Mayon, R., & Ding, H. (2026). Phase-tuned wave focusing by dual-parabolic breakwaters with a submerged secondary element. Communications Engineering. https://doi.org/10.1038/s44172-026-00777-3

Image Credits: AI Generated

DOI: 10.1038/s44172-026-00777-3

Keywords: Phase-tuned, wave, focusing, dual-parabolic, breakwaters, submerged, secondary, element, scientific research