ALT Affect is widely held to depend on interoception. This paper proposes that valence is the rate at which interoception moves a self-sustaining system's responsiveness relative to its terminal set (roughly, death). Responsiveness is formalised as the scale of the system's Jacobian and interoception as its internal inputs: receptors, signals and forecasts of its energy reserve. Changes in responsiveness that bypass interoception, such as brain damage, are not felt. Language models have no valence because they are not self-sustaining. The chain rule splits valence over the system's parts and the causes that affect its internal inputs. With the log scale, these splits account for need-dependent pleasure, diffuse hunger and localised pain, negativity bias, and relief when a threat fails to arrive. In a minimal model of a body: (1) Blocking the reserve signal leaves depletion unfelt, and driving it produces hunger with an undepleted reserve. (2) Removing receptors or their gain, or driving re

ALT Spike-field couplings measure the links between neuronal spiking and local field potential (LFP) and provide a tool to study how neural populations coordinate activity across spatial and temporal scales. While modern high-density electrophysiology enables the simultaneous recording of numerous LFP channels and neuronal units, this massive scale poses significant statistical and computational challenges for conventional pairwise coupling analyses. The number of channel-unit comparisons grows rapidly, and a coherent population-level structure can be obscured by high-dimensional coupling matrices. Generalized Phase Locking Analysis (GPLA) addresses this challenge by organizing spike-triggered LFP phase information into a complex coupling matrix and extracting dominant low-rank modes. These modes summarize whether a population is coupled to the field, which channels and units contribute to the coupling, and how their relative phases are organized. Here, we present PyGPLA, an open-source Py

ALT Uncovering low-dimensional representations of brain-wide neural dynamics and their relationship to behavior remains a central challenge. Here, we show that spontaneous brain-wide rotational dynamics constrain cue-evoked behavior and learning. To systematically establish these findings, we developed a dimensionality-reduction technique called Rotational Component Analysis (RCA) that optimizes the detection of low-variance rotational modes even within individual trials. We applied RCA to motor-cortical spike-train data from monkeys and humans, mouse cortex-wide calcium imaging, and human functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). In mice, a rotational mode identified from activity recorded at rest predicts cue-evoked behavior: the phase of rotation when a randomly timed cue arrives predicts whether the animal subsequently pulls the lever. We explain this surprisingly strong correlation using both a recurrent neural network (RNN) and a minimal two-dimen

ALT Functional connectivity changes across adult aging, but most evidence is cross-sectional, and covariance-based connectivity depends on blood oxygenation level dependent (BOLD) fluctuation amplitude. Covariance decline can reflect changing fluctuation amplitude, and its interpretation may depend on whether normalization uses signal amplitude or includes estimated noise. It is also unclear which longitudinal changes recur across brain states. We analyzed 852 adults (baseline age 36-89 years) from the Aging Adult Brain Connectome with repeated functional MRI (fMRI) during rest, inhibitory control, associative memory, and visual-motor processing (2,220 sessions; median follow-up 5.5 years). Baseline age and elapsed follow-up were modeled separately. Ten cortical networks were tested jointly across states, and covariance was normalized either by released signal amplitude or by a candidate total amplitude reconstructed from released signal and noise amplitudes. Native common covariance decli

ALT Spaceflight-associated motion sickness, including space motion sickness (SMS) and terrestrial readaptation motion sickness (TRMS), has presented as the most operationally limiting effect on the human in the days following a gravity transition. Published data suggest that astronaut head movements provoke symptoms, but those studies rarely controlled for them. Most previous studies use postflight retrospective reporting and have not quantified temporal dynamics or persistence of symptoms; no study has published motion sickness in a cohort of commercial flyers. Here, we comprehensively evaluate SMS and TRMS within commercial flyers (N=8; 4F, 4M) for the first time, across two commercial missions of short duration (Polaris Dawn and Fram2). Utilizing a standard reporting methodology, we find an 87.5% incidence of SMS, which persisted for at least three days on orbit in 25% of crewmembers. The symptoms included vomiting in 62.5% of crewmembers. Regarding TRMS, incidence was 50%, severity was

ALT Computational complexity theory provides a framework for analysing abstract models of computation, whereas computational systems operate as finite physical implementations whose behaviour is shaped by their architecture and physical substrate. In such systems, computational organisation cannot be directly characterised from algorithms or internal representations. Building on the use of reaction time distributions to constrain mechanisms in cognitive science, we introduce an empirical framework for investigating computation in physical systems by inferring computational mechanisms from observable computation time distributions. The framework combines empirical benchmarking, which characterises computational efficiency through the scaling of computation time with problem size, with distributional analysis, which treats computation time distributions as signatures of the stochastic dynamics generated by different computational mechanisms. We demonstrate the framework using a scalable visu

ALT Measures of signal complexity have become increasingly popular in electroencephalography (EEG), where they are often interpreted as indicators of neural information processing, dynamical richness, or conscious state. In contrast, classical measures of temporal dependence, such as the autocorrelation function (ACF), remain more directly linked to known physiological mechanisms and are easier to interpret. The present work revisits, for the EEG practicioner, the relation between normalized Lempel-Ziv complexity (LZC) and temporal autocorrelation in both synthetic and physiological EEG signals. First, synthetic time series exhibiting a broad range of temporal structure and complexity levels were generated using stochastic processes with varying degrees of temporal dependence. LZC and autocorrelation measures were then computed and compared. Across simulated signals, a robust inverse relationship was observed between normalized LZC and the first autocorrelation coefficient, indicating that

ALT Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal representations may not reflect individual human perception. In this work, we investigate whether we can fine-tune brain encoding models to better align their representations with individuals' similarity judgments. Using similarity judgments of 100 images from 8 participants in the Natural Scenes Dataset (NSD), we adapt a generic human similarity judgment model (DreamSim) separately for each participant to obtain personalized perceptual similarity models. These models are then used to obtain subject-specific similarity structure across images in their encoding model training sets. We fine-tune each participant's baseline encoder in two conditions, aligning its representations with either generic or personalized similarity judgments, with the same initialization and objective. We find that baseline brain encod

ALT Normative models chart how brain measures vary across the lifespan, expressing each individual as a deviation from a healthy reference. However, most regional models of cortical thickness disregard dependencies between cortical and subcortical anatomy, and transferring them to a new site typically requires retraining or a recalibration of location and scale alone. We condition lifespan norms of cortical thickness on subcortical volume, such that each deviation indicates whether an individual's cortex lies within the healthy range given their subcortical anatomy, and make them transferable. In 21,351 healthy individuals from 216 sites, conditioning on ten subcortical structures improved out-of-sample prediction on average, most for the amygdala and hippocampus. Refitting only the intercepts of all four distributional parameters on about 20 healthy individuals, as implemented in our open-source package NormGAMLSS, calibrated most of six unseen sites without retraining, for base and condi

ALT Biological function operates far from thermodynamic equilibrium, sustained by metastable structures that continuously cycle through synthesis, function execution, and degradation–consuming energy and exporting entropy in the process. Neural computation exemplifies this dissipative organization, relying on low-entropy structures engaged in dynamic processes that define key information transfer and processing parameters such as gain, threshold, and integration time. This perspective proposes that sleep serves as a systemic recalibration phase required to reset crucial signaling parameters that drift during dissipative processing. During wakefulness, neural activity progressively consumes and disrupts the organization of the very substrate from which it emerges, leading to parameter drift and reduced computational precision. Sleep maintains computational accuracy across activity cycles by recalibrating function-structure coupling–that is, by counterbalancing entropic drift. Rather than ex

ALT We introduce a statistical-mechanical framework for spiking neuronal systems in which entire spike trains, defined as spatiotemporal configurations of discrete events, are treated as microstates of the system. Instead of modeling explicit network dynamics, we construct an ensemble over complete spike histories and assign to each configuration an effective energy functional that quantifies its consistency with intrinsic synaptic interactions. This formulation leads to a Boltzmann-like distribution over spike configurations, where an effective temperature captures the influence of external inputs acting as a stochastic drive. Macroscopic states are described in terms of coarse-grained observables, such as population firing-rate densities, and arise from a competition between energetic contributions favoring interaction-consistent spike patterns and entropic contributions favoring spatio-temporal disorder. Within this framework, we primarily characterize the transition between oscillatory

ALT Awakening is reported across contemplative traditions yet remains poorly defined in science, where it is often conflated with transient states induced by meditation or psychedelics, obscuring its significance for theories of consciousness and cognitive architecture. We treat awakening as a functional construct capturing convergence across Hindu and Buddhist sources, phenomenological and psychometric findings, and neuroimaging evidence. Synthesizing these literatures, we find convergent evidence for reorganization across dimensions including self-related processing, affective reactivity, and temporal structure, while exposing limits of paradigms for detecting trait-level change. We argue that awakening is best understood not as a transient state but as a trait-level reorganization of experience, centered on a reduction in identification with mental contents rather than the acquisition of new content. To account for this contentless transformation, we propose the enactive-dereification m

ALT How mental training reshapes the temporal architecture of large-scale brain dynamics and how these mechanisms differ between healthy and clinically vulnerable populations remains poorly understood. Time-averaged neural measures collapse the multi-scale nature of brain activity, obscuring the dynamic processes through which behavioural interventions reorganise hierarchical brain function. Here, applying a sliding-window Temporal Irreversibility Index to source-reconstructed EEG across three complementary cohorts (n = 141), we decompose hierarchical brain trajectories into three orthogonal temporal descriptors and uncover two dissociable, mechanistically interpretable axes of intervention-induced plasticity. Cumulative meditation practice drives a universal, pan-cerebral reduction in the exploratory breadth of hierarchical dynamics, tracking dose-dependently with lifetime hours of practice. Temporal persistence is bidirectionally tuned by 8-week mindfulness-based interventions in a popul

ALT Identifying the neural underpinnings of intelligence is one of the major goals of modern intelligence research. However, few imaging studies explore the high extremes of intelligence, and most use only a single composite IQ score. This study used quantitative EEG recordings to investigate correlations between the EEG variables of individual alpha peak frequency, relative power, and coherence, and scores on verbal and perceptual reasoning tests in participants with IQs from the low-average to exceptionally gifted ranges. Overall, the results did not reveal any pattern of significant relationships between these EEG measures and the two psychometric tests. Since this study had only a small sample, heavily skewed towards the high-IQ end of the scale, no definitive conclusions about the EEG measures and verbal and perceptual reasoning can be drawn. Nevertheless, it reveals many opportunities for further research, and suggestions are made for progressing this topic in the future.

ALT Biological neural circuits contain both unidirectional and reciprocal connections, but how reciprocity affects circuit computation remains unclear. We varied reciprocity h̊o across 21 levels in recurrent-network simulations while holding network size, connection density, and directed-edge count fixed. Low-reciprocity networks produced more diverse stimulus-driven activity and performed better on four continuous-input tasks, whereas higher reciprocity generally improved performance on three delayed-memory tasks. Increasing reciprocity reduced online memory capacity and accelerated recovery from perturbations. A post-cue noise experiment showed that higher reciprocity suppressed internal noise propagation but made cue-evoked states less distinguishable, and component matching separated the contributions of cue separation and within-cue dispersion to readout robustness. Within this ensemble, reciprocity thus mediates a three-way trade-off among representational capacity, local stability,

ALT Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler. Here we ask how observational resolution shapes the apparent structure of neural computation. We introduce a scale-indexed decomposition that separates the predictive information captured by direct dependencies from the residual outside that model family. Exact constructions show that projection can convert higher-order dependence into pairwise structure. Projection can increase the direct fraction of surviving information even as total predictive information decreases. A schematic branch-to-soma cascade grounds these effects in local coincidence detection, saturating integration and somatic mixing. Resolving organized nonlinear subunits improves approximation and raises effective representational rank. In a nested hierarchy, these gains occur without increasing information about the target. This distinction separates information availability from access by a restricted readout. A point-

ALT Stress reshapes cortico-limbic dynamics across multiple timescales, with important consequences for healthy and pathological arousal responses. We develop and analyze a Wilson-Cowan model of prefrontal-amygdala circuitry, incorporating excitatory and inhibitory populations involved in emotional regulation. The effects of stress cortisol are represented as history-dependent modulations of two stress-sensitive interactions: recurrent prefrontal excitation and inhibition of basolateral amygdala principal cells. Using numerical simulations and bifurcation analysis, we examine how this self-interacting system organizes transitions between stable activity and infra-slow oscillations, and how this structure changes with cortisol-related temporal memory. To distinguish the effects of memory temporal distribution from those of its timescale, we compare weak and strong Gamma kernels with a discrete-delay limit, representing progressively different ways in which past activity can influence curren

ALT In Alzheimer's disease, the amount of pathology in the brain is an imperfect predictor of how much function is lost. Pathology leads to that loss through neural circuits, but these cannot be observed neuron by neuron in patients. This study therefore simulated the central nervous system of the adult male Drosophila melanogaster: 166,700 integrate-and-fire neurons wired by the MaleCNS connectome. The simulator reproduced a published fly brain model (rate correlation 0.9997). Three kinds of damage reported in amyloid-β flies, a lowered spike threshold, synapse loss and neuron loss, were imposed with assumed magnitudes, and circuit responses were measured in 1,424 simulated flies. The number of neurons lost was a poor guide to the deficit, which depended partly on how many synaptic contacts went with them and whether they included input neurons or one command neuron. With the same 12% of neurons removed, odour-evoked whole-brain activity was 93%, 47% or 31% of the healthy value when damag

ALT Contemporary accounts of affect emphasize different perspectives, ranging from the regulation of bodily states in allostatic approaches to the relational and sense-making character stressed by enactive theories. Here, we show that affect can be made explicit within the sense-making phase transition framework previously introduced for the perception of an inner sensation. Within this same formal structure, each stable minimum of the sense-making potential defines an affective state: its position determines valence, while its depth determines arousal. Affect therefore is not introduced as an independent ingredient, but emerges as the associated state in the valence-arousal space, providing a link between allostatic and enactive approaches to affect. This formulation distinguishes valence intensity from arousal, allows for dominant and secondary affective states, and naturally accommodates mixed-valence configurations beyond a strictly bipolar description. Revisiting representative landsc

ALT Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology to learn and adapt to their environment or compensate for lesions. Yet, in contrast to synaptic plasticity, structural plasticity is less well studied and understood. We here present the current state of knowledge as well as open problems in the field along three guiding questions: (1) What regulates structural plasticity in biological neural networks? (2) What are the functional consequences and advantages of structural plasticity in adult neural networks? (3) How can our understanding of structural plasticity be translated into clinical and technological applications? We argue that understanding structural plasticity is a problem involving multiple temporal and spatial scales. Solving this problem thus requires collaborative efforts of experimentalists, theoreticians as well as computational modelers, and h