Current neuroscience situates cognition within cortical and (to a less extent) subcortical areas, relegating receptors to passive relays. We propose a receptor-centered framework in which receptors act as constitutive determinants of neural architecture and cognitive experience, supported by empirical studies that substantiate our interpretation. Opsin gene therapy altering color vision in adult primates, as well as cross-modal reorganization in congenital or acquired human blindness, show that natural or artificial modifications of receptor repertoires can reshape cortical maps and perception. Mechanoreceptors and proprioceptors can sustain the representational scaffolds for agency, motor planning and body schema, while the interoceptive receptors that detect respiratory, cardiovascular and visceral states can modulate affect, memory and perceptual awareness. Also, we propose that gut bacteria may contribute to cognition through receptor-mediated signaling, where microbial metabolites interact with neural, immune and endocrine receptors to influence mood, motivation and higher cognitive processes. We argue that receptor guidance may extend upward into cognition: mental imagery, sensory substitution and synesthesia point towards imagination and abstract thought as recombination of receptor-derived primitives. Comparative neurobiology reinforces our claims, showing that distinct receptor repertoires yield species-specific perceptual worlds such as echolocation in bats, polarization vision in cephalopods or magnetoreception in birds, each shaping unique cognitive landscapes. Overall, cognition could be described as a multilevel process in which cortical activity is driven by receptor-encoded informational templates that are established across phylogeny, ontogeny and individual experience. To think is to reconfigure receptor traces, while to imagine differently is to sense differently.

Human consciousness, though often described in abstract terms, is grounded in identifiable physiological mechanisms that can be analysed and measured via empirical methods. Drawing on interdisciplinary evidence from neuroscience, physiology and systems biology, we propose a provisional framework aimed at characterizing the physical and biophysical features that may underlie human consciousness. We review current methods for quantifying these biophysical correlates, highlighting the potential roles of electrical activity, metabolic thresholds, thermodynamic constraints, ionic regulation and network dynamics in sustaining conscious states. We also consider the contributions of non-neuronal cells such as astrocytes and microglia, alongside the modulatory influences of peripheral inputs, including gut-brain interactions and cardiovascular and respiratory rhythms. We then examine the physiological dynamics underlying shifts in consciousness by integrating clinical data from anaesthesia, coma and sleep with neurophysiological and biochemical measurements. This synthesis allows us to identify a set of quantifiable parameters that characterize the conscious brain, including oscillatory coherence, cerebral metabolic rate, spike timing precision and ionic stability. We emphasize the importance of methodological convergence, whereby the integration of neuroimaging, electrophysiology and computational modelling enhances analytical robustness, improves interpretability and enables cross-validation of findings. Next, we conceptualize consciousness within a multidimensional threshold space, where varying degrees of awareness emerge from biophysical and physiological interactions. Overall, our approach proposes an operational definition of consciousness based on identifiable thresholds and interdependent physical parameters, aiming to support the integration of diverse findings within a coherent systems-level framework grounded in empirical evidence and clinical observations.

Cerebrospinal fluid (CSF) flows play a main role in maintaining brain homeostasis, supporting waste clearance, nutrient delivery and interstitial solute exchange. Although current models emphasize mechanical drivers such as cardiac pulsation, respiration and ciliary motion, these mechanisms alone fall short of explaining the nuanced spatiotemporal regulation of CSF flow observed under physiological and pathological conditions—even when accounting for the glymphatic framework. We explore the hypothesis that electrostatic forces arising from charged cellular interfaces may contribute to CSF movement through electro-osmotic mechanisms. We begin by examining the biological basis for surface charge in the brain, highlighting the presence of charged glycoproteins, ion channels and dynamic membrane potentials on ependymal and glial cells interfacing directly with CSF pathways. Next, we describe key principles of electro-osmosis in confined geometries, emphasizing how nanoscale surface charges can modulate fluid motion without mechanical input. Drawing from nanofluidic research and electrohydrodynamic theory, we argue that the conditions required for electro-osmotic coupling, i.e., ionic fluid, narrow conduits and patterned surface charge, are present within brain microenvironments. To test plausibility, we present computational simulations demonstrating that surface charge patterns alone can induce structured fluid flow and solute transport, including nonlinear transitions and oscillatory behaviours that resemble physiological rhythms. These findings support the idea that electrostatics may play a modulatory role in CSF regulation, complementing mechanical drivers. Overall, by integrating concepts from neuroscience, biophysics and nanotechnology, we propose a testable, mechanistically grounded hypothesis reframing CSF dynamics as electrohydrodynamically sensitive processes, with potential implications for understanding brain function and dysfunction.

Understanding intelligence-related variations in electroencephalographic (EEG) activity requires advanced mathematical approaches capable of capturing geometric transformations and long-range dependencies in neural dynamics. These approaches may provide methodological advantages over conventional spectral and connectivity-based techniques by offering deeper insights into the structural and functional organization of neural networks. In this study, we integrate Clifford algebra, Noether’s theorem and fractional calculus to analyze EEG signals from high- and low-IQ individuals, looking for key intelligence-related differences in cortical organization. Clifford algebra enables the representation of EEG signals as multivectors, preserving both magnitude and directional relationships across cortical regions. Noether’s theorem provides a quantitative measure of symmetry properties linked to spectral features, identifying conserved functional patterns across distinct brain regions. Mittag-Leffler functions, derived from fractional calculus, characterize long-range dependencies in neural oscillations, allowing for the detection of memory effects and scale-invariant properties often overlooked by traditional methods. We found significant differences between high- and low-IQ individuals in geometric trajectories, hemispheric connectivity, spectral properties and fractional-order dynamics. High-IQ individuals exhibited increased spectral asymmetry, enhanced spectral differentiation, distinct geometric trajectories and greater fractional connectivity, particularly in frontal and central regions. In contrast, low-IQ individuals displayed more uniform hemispheric connectivity and heightened fractional activity in occipital areas. Mittag-Leffler fractional exponents further indicated that high-IQ individuals possessed more varied neural synchronization patterns. Overall, our multi-faceted approach suggests that intelligence-related neural dynamics are characterized by an asymmetric, functionally specialized and fractionally complex cortical organization. This results in significant differences in network topology, efficiency, modularity and long-range dependencies.