ALT We study the dynamics of recurrent neural networks with random couplings, sublinear odd activation functions, and temporally correlated noise. Using large N methods, we determine stationary correlations and distinguish the loss of local linear stability from the onset of chaos, characterized by a positive largest Lyapunov exponent. These thresholds delimit an intermediate regime in which local instability coexists with asymptotic stability under a common noise history. Their separation depends on both the noise correlation time and the asymptotic growth of the activation function. We show that internal noise with any finite correlation time cuts off the divergence of the network's intrinsic correlation time taucorr at the edge of chaos; it exhibits a critical scaling taucorr,max propto 1/sqrtD₀, where D₀ is the integrated noise strength. Static random input instead produces coincident thresholds and a divergent correlation time. We also calculate the frequency-dependent participation d

ALT Some of the most valued forms of human creativity arise between people-in conversation, collaboration, and improvised music-yet how two minds jointly regulate the flow of novelty in real time remains unknown. Here we examined the information dynamics of interpersonal improvisation using 986 professional jazz improvisations. Pitch-based fluctuations of uncertainty and surprise behaved as signatures of individual identity: the two partners differed within 90% of takes, performers were identifiable from single solos and the separation between partners persisted undiminished across turns and takes. Rhythm-based fluctuations showed the reverse organization: partners were statistically indistinguishable within a take yet shifted together from take to take, and this shared state drifted gradually as each performance progressed, with each performer moving away from their own past at the same rate as from their partner's. Beyond joint drift, solo hand-offs revealed turn-by-turn complementarity:

ALT Simulation-based inference (SBI) provides a powerful way to fit mechanistic models to data, but offers little insight into which information in the data shapes the inferred parameter values. It could therefore benefit from robust and inexpensive interpretability methods. Learned weighting mechanisms such as feature gates provide a potential route to low-cost interpretability, although their interpretation assumes that predictive training reveals which inputs drive performance. We examined this assumption in a computational neuroscience setting, where neural posterior estimation (NPE) is used to infer four physiological parameters of a dynamical model of interacting neuronal populations (Jansen-Rit). We tested whether the learned gate weights captured feature importance by comparing them with a retrain-based ablation benchmark, in which each feature was removed in turn and the NPE retrained. We considered two jointly trained gates: independent sigmoid weights and competing softmax weigh

ALT Most neuroscientific accounts of consciousness assume that conscious experience is constituted by the information conveyed between neurons. The physical-chemical details of the metabolic and physiological processes that convey this information are correspondingly presumed to be relevant only insofar as they support the organization of information processes. We describe an alternative perspective that denies this presumed independence of the processing of neural information and the metabolic-physiological details of these processes. We call this alternative paradigm the theory of neuro-metabolic entanglement (NME). NME proposes that mental experience exemplifies the inseparability of the organization of neural information processes from the constraints imposed by the material-energetic properties of the neural medium itself. Central to this account is a recursive, Möbius-like loop of neural connections that entangles patterns of forebrain information processing with midbrain regulatory

ALT Background: Gamma entrainment using sensory stimulation (GENUS) is under study as a treatment for neurological disease, but cortical engagement is variable. Vagus nerve stimulation (VNS) modulates cortical state and sound-evoked beta-gamma activity, suggesting that it may strengthen responses to rhythmic sensory input. Objective: To determine whether VNS strengthens auditory steady-state responses across beta and gamma rates and whether the effect depends on cortical region and sensory drive. Methods: Electrocorticography (ECoG) was recorded from core and belt auditory cortex, insular auditory field (IAF), and anterior insular cortex (AIC) in seven isoflurane-anesthetized rats before and 90 min after initiation of 30-Hz cervical VNS. Responses to 500-ms click trains at 20 and 40 Hz were quantified using inter-trial phase clustering (ITPC), event-related spectral perturbation (ERSP), and debiased weighted phase-lag index (WPLI). Results: VNS increased click-evoked P1-N1 amplitude. Durin

ALT A challenge in exploring the psychology of human uncertainty is its measurement and quantification. Prior work has focused almost exclusively on probability, while the formal theories developed in philosophy are rarely tested. We tested Wolfgang Spohn's ranking theory, a normative account in which belief is graded by ordinal ranks of disbelief that combine by minimization rather than addition, as a descriptive model of human belief. Across six experiments, participants assigned ranks of disbelief to propositions involving personal experience, chance devices, sequential learning, and an Ellsberg urn. Ranks were reliable and, through the theory's two-sided rank function, distinguished belief, disbelief, and suspension of judgment. Participants respected the Law of Negation, never disbelieving both a proposition and its complement, in every task except a lottery. Disjunctions did not follow the theory's Law of Disjunction. Instead of taking the minimum rank of the disjuncts, participants

ALT Friston's free energy principle holds that a system separated from its environment by a Markov blanket—a boundary whose states statistically screen its interior from its exterior—can be treated as performing approximate Bayesian inference, and that such blankets arise wherever coupling is short-range. We test this blanket condition in a concrete nonlinear pattern-forming system: self-replicating spots in a reaction-diffusion model that persist, compete, and reproduce. Directed boundary exchange and timescale separation hold up reasonably well, with qualifications. A flux-defined blanket does not screen interior from exterior. Flux removes only part of the linear dependence, and a nonlinear test shows that conditioning on flux increases the dependence, consistent with conditioning on a collider, since flux is a joint function of concentrations on both sides of the boundary. The dependence is concentrated within about one organism radius of the boundary. A band of the surrounding field t

ALT Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semantic commonality remains unclear. Here we developed a multilingual brain decoding framework and analysed brain recordings from 227 participants spanning Chinese, English and French. We found that cortical semantic representations exhibited consistent semantic geometry relationships across languages and individuals, revealing a shared cortical semantic geometry. Importantly, the cross-language consistency of cortical semantic geometry increased from sensorimotor to higher-order association cortex, forming a shared semantic geometry gradient closely aligned with the principal cortical functional gradient. Our work establishes generalizable multilingual brain decoding and identifies stable cortical semantic geometry as a neural basis for semantic commonality across languages.

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