Papers

Reproducible biomarkers that monitor motor and cognitive dysfunction in amyotrophic lateral sclerosis (ALS) are needed. Previous work reported reduced beta-band event-related desynchronization (ERD) and attenuated post-movement event-related synchronization (ERS) in frontal and parietal channels during the sustained attention to response task (SART). We aimed to validate these findings in Dutch and Irish cohorts. A randomized SART with 128-channel electroencephalography (EEG) was performed in Dutch (ALS n=63; matched controls n=64) and Irish (ALS n=36; controls n=36) cohorts. Non-phase-locked activity was quantified using complex Morlet wavelets to compute baseline-normalized post-stimulus ERD and ERS. Group differences and EEG associations with task performance were examined for Go and NoGo trials. In both control cohorts, the SART elicited theta ERS and alpha- and beta-band ERD, followed by rebound beta ERS across the frontoparietal axis, with greater theta ERS, stronger alpha ERD, and weaker beta ERS on NoGo than Go trials. In contrast to previous null findings, people with ALS showed reduced Go and total accuracy in both centres (p<0.002) and slower Go responses in the Irish cohort (p=0.02). The ALS cohorts differed in NoGo accuracy, Go response time, and anticipation errors (all p=0.03). Replicating prior work, the Irish ALS cohort showed reduced beta ERD and ERS versus controls, with no such differences in the Dutch cohort. Greater beta ERS was associated with faster Go responses in both centres (p<0.011). ERD and ERS changes replicated in the Irish but not the Dutch cohort. Cohort differences in these metrics reflected, at least in part, varying functional impairment. Reproducible control findings and consistent associations with motor performance indicate reliable capture of cortical motor network dysfunction, supporting these measures as EEG markers of motor decline in ALS.

Spreading processes on networks often interact with other dynamics on the same nodes. Neurodegenerative disease provides one example: pathological proteins spread through anatomical connections, while neuronal activity influences and is altered by their spread, forming a spreading-activity feedback loop. However, models coupling pathological protein spreading and neuronal activity have largely focused on linear feedback between the two processes. Here we show that nonlinear feedback can fundamentally change the invasion dynamics in a susceptible–infected–susceptible spreading process coupled to a co-evolving activity process. On general weighted networks, the strength and shape of the feedback may generate finite-amplitude invasion thresholds, hysteresis, and endemic multistability. On regular graphs with homogeneous dynamics, we rule out periodic solutions and show that the degrees of polynomial coupling functions bound the number of endemic states, while monotone couplings require reinforcing feedback for multistability. We test these predictions in simulations of a stochastic spiking neuronal network described by quadratic integrate-and-fire dynamics, where we recover both finite-amplitude invasion thresholds and endemic bistability. These results show that feedback from activity processes can create hysteresis and multistability in network spreading dynamics. In neuroscientific applications, our work suggests that neuronal dynamics may act as a control point in neurodegenerative disease, with even transient changes in activity capable of tipping the brain between health and disease.

We propose a formal account of how structured, shared knowledge about social relationships shapes action interpretation. The model represents relationships as constraints in a social environment, analogous to boundaries or obstacles in a physical environment and operating within the same generative model, but exerting distinct constraints on action. As an initial test of this framework, we draw on research across the social sciences to capture in the models how one dimension of relationships – formality versus intimacy – shapes how people interpret interpersonally vulnerable behavior. We test this account in stories of naturalistic everyday situations, extending structured models of action understanding to open-ended contexts. Across six preregistered experiments (N = 1,554), the model captures people's inferences about desires, physical environments, and social relationships. This work formalizes how relationships can constrain – and be revealed through – everyday action.

Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social interactions. Large language models (LLMs) demonstrate behaviour consistent with humans on theory-of-mind tasks, yet whether these models can guide adaptive behaviour through mentalization is unknown. Here we use two economic games with cognitive computational modeling to uncover the latent strategies underlying mentalization in LLMs. We tested individual LLM agents across four model families, DeepSeek, GPT-4.1, GPT-5 and Gemini 2.0 Flash (N = 2,099), against opponents of varying sophistication and examined whether a prompting strategy designed to elicit strategic reasoning improved performance. We benchmarked results against human participants (N = 251) as a comparative measure. Across both games, LLMs showed clear behavioural and computational signatures of mentalizing that differed markedly by model provider and size. Strategic prompting generally improved performance by inducing more sophisticated reasoning, yet the extent of the benefit differed across the two tasks. Last, GPT-5 agents flexibly adapted their recursive depth of reasoning to increasingly sophisticated opponents, demonstrating superior performance to human participants. Collectively, we demonstrate different capacities for mentalization across LLMs, and highlight cognitive computational modeling as a formal method for assessing comparative intelligence across humans and machines.

The coordinated movement of animal groups is one of the most widespread social behaviors, which are generally attributed to high-order cognitive processing in the brain. Yet, collective coordination can seemingly emerge from rapid, local interactions between individuals, suggesting the existence of decentralized mechanisms of online coordination that remain to be identified. Here, we show that a low-order spinal sensorimotor circuit is required for real-time social coordination during schooling in zebrafish. Central to this circuit are intraspinal proprioceptive neurons that detect local body bending and deliver direct, curvature-based inhibition to precisely time the locomotor network. Combining electrophysiology, calcium imaging, optogenetics, and behavioral analysis, we show that this circuit encodes both self-generated (egocentric) and neighbor-induced (allocentric) body bending signals, enabling fish to match the phase of their swimming to the wakes of their neighbors (vortex phase matching). In a neuromechanical model and physical robot, this single feedback loop is sufficient to generate vortex phase matching and to lower the energetic cost of swimming. Disrupting this circuit uncouples neighboring fish and abolishes schooling behavior. These results show that a spinal circuit dynamically synchronizes individuals through simple, local interactions, revealing how low-order mechanisms can drive the emergence of coordinated group behavior.

There is growing international interest in generative AI (GenAI) literacy and its assessment among high school students, but objective assessment in this population remains underdeveloped. This article reports the iterative development and validation of the GenAI Literacy Test (GenAIT), an 18-item multiple-choice test measuring high school students' conceptual knowledge about GenAI, with content spanning technical, practical, and human-impact domains. Expert review of relevance, clarity, and comprehensiveness provided evidence of content validity. In a large-scale survey of 7432 Estonian high school students, we evaluated the psychometric functioning of the Estonian-language GenAIT using confirmatory factor analysis, classical test theory, and item response theory. Results supported approximate unidimensionality, broadly adequate reliability for group-level research (marginal reliability = .72, KR-20 = .69), and good fit of a three-parameter logistic model (RMSEA = .013, TLI = .987, CFI = .990, SRMSR = .021). Measurement precision was sufficient for the majority of students but varied substantially across the latent trait, with lower precision for lower scoring students. GenAIT is therefore more suitable for group-level research than high-stakes individual classification. GenAIT scores were unrelated to perceived usefulness and perceived ease of use, and negatively associated with LLM use frequency, suggesting that frequent use and favorable perceptions of AI should not be treated as proxies for conceptual understanding.

Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or fingerprints, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.

Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimensional responses of individual neurons. Our aim is sample efficiency-models that decode well from limited data and at small parameter budgets. In a standard Transformer layer, the feed-forward block applies the same operator to every token. We suggest a von-Neumann inspired hypothesis of efficient computation as an alternative for neural decoding: a controller decodes an instruction and then executes a token-specific operator; the usual realization-a soft mixture of experts-only blends their outputs, not operators. We introduce a von-Neumann State-Space Transformer (VN-SST), a memory-augmented Transformer whose feed-forward block is a low-rank instruction bank: a shared base operator plus a small set of learned low-rank instructions, from which a per-token code synthesizes the weight matrix actually used at that token. The code is read from a low- dimensional projection of a carried state-space memory, so a slow latent trajectory acts as an instruction pointer-mirroring how low-dimensional dynamics may route cortical computation. On three motor-cortex neural-decoding benchmarks, VN-SST is far more data-efficient than a modern Transformer, each jointly predicting spikes and decoding behavior. This model wins by a wide margin on the scarcest benchmark, leads on the other two, and turns longer context into rising rather than falling accuracy. We evaluated that the network compresses a large instruction bank to a few bits per token, so program capacity acts as a control channel, not an accuracy lever. The same model is also more parameter-efficient on two small text benchmarks used for language modeling (LLMs), suggesting a generic mechanism.

The entropy production rate (EPR) quantifies irreversibility of a nonequilibrium steady state, yet standard formulas obscure how a complex interaction network generates it. For multivariate Ornstein-Uhlenbeck dynamics on such networks, we express the EPR as a quadratic form in antisymmetric matrices measuring the nonreciprocity of aggregate directed walks at every length, and, equivalently, as two weighted-walk quantities: pairs of directed walks sharing both endpoints, and directed closed walks. For diagonalizable interactions, an exact correspondence translates these walk quantities into eigenvalues and biorthogonal eigenvector overlaps. Across dense, sparse, and deep acyclic random interactions satisfying matched-walk conditions, the mean EPR per node universally follows the square-root law $\phi_*(g)=1-\sqrt{1-g^2}$, where $g \in [0,1)$ parametrizes the interaction strength. Deep acyclic interaction matrices are nilpotent, with all eigenvalues fixed at zero for every $g$, yet, as their depth increases, their mean EPR per node approaches $\phi_*(g)$. Thus, the square-root law arises from directed walk properties, rather than from a shared spectral density or specific network topology.

Prenatal maternal distress has been linked to differences in offspring brain and behavioural development, but is rarely framed computationally. Predictive-processing (PP) accounts hold that perception, cognition, and action arise from the interplay of incoming input and internally generated predictions, governed in part by precision. We reappraised eleven publications from two non-clinical prenatal cohorts for a coherent PP-compatible developmental pattern. Eight Leuven publications spanned adolescence to adulthood: externally cued response inhibition was largely preserved, whereas differences emerged when task goals, stimulus relevance, expected outcomes, and response policies had to be generated or maintained endogenously. At age 28, higher prenatal anxiety was associated with lower radial and mean diffusivity in the left superior posterior corona radiata, lower Vocabulary scores, weaker medial prefrontal to left inferior frontal connectivity, and greater unpleasantness of induced dyspnoea. Three Tilburg ERP publications covered infancy and early childhood: higher prenatal anxiety was associated with increased processing of repeated standard tones, of fearful over happy vocalizations, and of neutral over pleasant or unpleasant pictures. Maternal mindfulness, the only positive prenatal characteristic examined, showed the opposite association for the repeated standard, suggesting that positive and distress-related states may act differently on shared mechanisms. Across cohorts, findings were compatible with differences in how relevance and processing resources are allocated to predictable, threat-related, ambiguous, contextual, feedback-related, and bodily information. Because these publications did not formally estimate precision, prediction errors, or priors, we treat them as hypothesis-supporting rather than a formal test of the account.

Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with which a face is perceived. We empirically test this hypothesis by training variational autoencoders on four face datasets without attractiveness supervision and evaluating their representations on the 597 faces from the Chicago Face Database. Across models, human attractiveness ratings closely aligns with the direction defined by the VAE evidence lower bound (ELBO) in rate-distortion space. Independently learned latent spaces contain an attractiveness direction that transfers strongly across random initializations and training data. We also find that attractive faces are more prototypical in both shape and latent space. Our results connect classic accounts of aesthetics with learned generative models and provide empirical support for a variational interpretation of the processing fluency theory of aesthetic pleasure.

How does an intelligent visual system combine what objects look like with how they move while remaining robust as appearance changes? We addressed this question by comparing human perception and neural activity in macaque inferior temporal cortex with representations from image- and video-based neural networks spanning recognition, segmentation, optic-flow processing and predictive world modeling. Temporal integration improved object representations, but most video recognition models generalized poorly when appearance was disrupted while motion structure was preserved. Humans and macaque IT remained robust. Notably, predictive world models combined strong cross-appearance generalization with the closest correspondence to IT, outperforming other video-modeling approaches in neural fidelity. Yet no model reproduced the cortical transformation from early appearance-dominated responses toward later appearance-invariant motion coding. These results identify progressive integration of motion into object representations as a principle of robust dynamic vision and implicate predictive learning as a promising route toward realizing this computation in artificial systems.

Over the past decades, it has become increasingly clear that the complex morphology of cortical neurons is more than just a quirk of evolution, and that dendritic compartments serve as computational elements in their own right, rather than just providing connections between nerve cell bodies. While most computational studies discuss the enhanced representational capabilities of multi-compartment models as compared to point neurons, we focus here on the implications of neuronal morphology for synaptic plasticity. We argue that the ability of single neurons to simultaneously encode multiple pieces of information gives synapses local access to more than just the classical Hebbian pre- and postsynaptic terms, and with much greater specificity and reaction speed than permitted by other globally modulated factors. Based on a comparative review of recent dendritic learning models, we show how such neuronal compartmentalization can provide synapses with the means for calculating various forms of error signals, which in turn give rise to powerful real-time and fully local instantiations of deep learning through gradient descent. Implemented within cortical microcircuits capable of propagating and manipulating these errors, compartmentalized neurons thus ultimately enable the learning of far more complex tasks than are achievable by globally modulated Hebbian plasticity alone.

Our capacity to maintain focus on task-relevant goals over time is constrained because attentional states wax and wane moment to moment. One theoretical account posits that temporal filling-in - filling temporal blank intervals between target stimuli - reduces attentional fluctuations to enhance sustained attention. This proposal, however, lacks direct empirical validation given the difficulty of tracking attentional fluctuations via behavioral measurements. To resolve this gap, we combined a visual timing task that assessed the behavioral benefit of temporal filling-in with electroencephalographic recordings of visual steady-state evoked potentials (SSEPs), which tracked neural responses associated with periodic attentional fluctuations entrained to the rhythmic visual sequence. Behavioral improvements in sustained attention via temporal filling-in were associated with reduced attentional fluctuations, as indexed by attenuated visual SSEP amplitudes. The findings highlight a fundamental neural mechanism whereby temporal filling-in reduces attentional fluctuations to stabilize sustained visual attention over extended periods.

Hippocampal replay is a phenomenon observed in mammals and songbirds where neural activation sequences experienced during wakeful periods are repeated during rest or sleep. This mechanism is believed to play a crucial role in episodic memory consolidation, retrieval, and planning. Interestingly, replay can occur in both forward and reverse temporal orders and across a wide range of increased speeds. The learning of activation sequences has traditionally been modeled by temporally asymmetric Hebbian plasticity rules, which explain replay as a chain of activity. The inability of such rules to strengthen backward connections has led to the widespread belief that they cannot account for bidirectional replay. Existing theoretical work therefore explains reverse replay through symmetric connections, which would be formed through symmetric plasticity rules. We propose a delay-coupled neural field model of CA3, where activity propagates as a wave in a ring, and replays occur both forwards and backwards at different speeds. Our model can learn bidirectional replay after a single noisy exposure to a stimulus with either symmetric or asymmetric plasticity. We derive a low-dimensional description of the dynamics and analyze the stability of possible replay speeds.

The standard memory models for neural networks are variants of the Hopfield network, where feature representations are stored as vectors in a matrix. Retrieval happens based on similarity between an input vector and the set of stored vectors in a content-addressable manner such that the network evolves towards the closest stored attractor. In other words, the useful property of addressing items in memory directly by index is lost in Hopfield-style neural network models ("associative memory"). In this extended abstract, we present a new model which is extremely simple, derived from biological observation, yet introduces a significant conceptual advance and technical benefits. The goal is to establish adaptivity based on the neuron-centric hypothesis: Plasticity is organized by the neuron which regulates its own synapses. Accordingly we implemented a localist, neuron-centric one-shot learning method and applied it to a simple pattern classification problem (MNIST). We were looking for the existence of high information neurons, to act as indices into the representations. The idea was that we would be able to restore full patterns by indexed retrieval, instead of associative vector retrieval from attractors.

The scientific study of consciousness frequently stalls on ontological debates regarding the "Hard Problem." This paper proposes a pragmatic pivot. Rather than asking what consciousness is metaphysically, we ask how modeling conscious access as a specific computational transformation may address existing bottlenecks in neuroscience and artificial intelligence. We introduce the Continuous/Discrete (C/D) framework, which holds that the brain implements two distinct processing regimes: System C, a distributed sensory-motor network operating over continuous, high-dimensional manifolds, and System D, a centralized engine structured around discrete, scale-invariant symbols. We argue that conscious access requires a structure-preserving translation between these regimes, which maps localized continuous states onto discrete symbolic tokens, coupled with an inverse projection that grounds those tokens back into sensorimotor dynamics. By formalizing conscious access as this continuous-to-discrete conversion, we derive a unified set of testable predictions centered on representational geometry, specifically, on a measurable collapse from graded similarity structures to low-dimensional categorical equivalence classes. These predictions explicitly differentiate our account from Global Neuronal Workspace Theory, Integrated Information Theory, Predictive Processing, and Higher-Order Theories, shifting the focus from ontological status to computational mechanism. Beyond neuroscience, the framework provides a principled architecture for neuro-symbolic artificial intelligence. We argue that treating conscious access as translational computation offers a pragmatic, empirically tractable pathway forward, clarifying what conscious states functionally accomplish without requiring resolution of the hard problem of phenomenology.

Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable. We therefore treat silent reading as a scalable proxy task and ask how much lexical and semantic information a contrastive decoder can extract from it. We report an open-vocabulary analysis of approximately 240,000 word presentations recorded from a single densely-sampled participant across 393 runs (ca. 49 h) of 19-channel dry-electrode EEG. Words from continuous narrative text were presented in rapid serial visual presentation, with typography randomised on every trial to partially decorrelate word identity from low-level visual form. A convolutional EEG encoder, optionally followed by a causal transformer, was trained with a CLIP-style contrastive objective to align short EEG windows with hidden-state embeddings of the presented word taken from a large language model. Decoding, evaluated as word-grouped top-10 retrieval against permutation baselines, was reliably above chance, extended to mid-frequency and rare words, and scaled log-linearly with training-data volume with no sign of saturation. Removing occipital and posterior-temporal electrodes reduced the word-level gain by roughly one third but left context tracking unchanged. Control analyses separate word-level decoding from narrative context tracking and from a non-neural positional prior introduced by the transformer's positional embedding. These results establish that open-vocabulary word-level information is recoverable from EEG during silent reading, and that decoding is data-limited rather than saturated.

AI agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the claim is limited to what the evidence supports. Agents may reproduce failures including selective analysis, premature declarations of success and optimization of imperfect criteria. We present Brain Researcher, an agentic research harness operating in a neuroimaging researcher's computational environment under rules for admissible analyses, required checks and claim scope. In benchmarks, Brain Researcher increased first-choice tool-selection accuracy across seven models by 70.2 percentage points (23.3% without it versus 93.6% with it) and verifiable grounding from 4.6% to 22.0%. In collaborator-led and self-evolving studies, multiverse analyses exposed analytic-choice sensitivity, and scientific review classified claims as accepted, qualified, revised, blocked, rejected or deferred. By linking decisions to evidence and provenance, Brain Researcher embeds methodological judgment within the workflow, not after it.

Integrated Information Theory (IIT) proposes that all quality is structure: the quality of every experience can be characterized as a phenomenal structure and accounted for as a causal structure. The IIT method can be called intrinsic in that, first, it starts by characterizing the intrinsic structure of a single experience in an absolute sense (not relative to other experiences) and, second, it preserves this intrinsic perspective when accounting for experience in physical terms. By contrast, the method of the Qualia Structure paradigm (QStr) can be called extrinsic in that its main tool is relational characterization, used to investigate both the quality of experience and the corresponding neural or information structures. This chapter gives conceptual clarity to IIT's intrinsic method, assesses the theoretical compatibility between QStr and IIT, and sketches concrete ways QStr can complement IIT. We argue that QStr and IIT share experience and its structure as their explanandum, while their methodological starting points and explanans are distinct yet complementary. QStr can bolster IIT's current research program by offering novel ways to approach narrow qualia (e.g., color), which are notoriously difficult to decompose through introspection. QStr can also supply IIT with new mathematical tools-for example, from category theory and metric geometry. In turn, IIT may help ground QStr's relational, extrinsic structures in absolute, intrinsic structures.

This paper develops a phenomenology-first approach to artificial consciousness by reframing consciousness as the subjective experience enacted through an agent's interface with the world. We shift the methodological focus to first-person structures, modeled mathematically by categories derived from Q-networks to capture actions and phenomenological invariants. In this framework, Q-networks are conceptualized as relational interfaces encoding agent-world interaction, analogous to how the dynamical states of a computer depend on its sensory inputs, previous states, and actions. Our work provides a rigorous framework for interface consciousness to describe computational systems that embed information-processing into phenomenological structure. The approach aligns with 4E approaches to cognition by emphasizing enactive, embedded, and extended dimensions of experience. The paper thus offers a principled, relational, and phenomenological account of artificial phenomenology grounded in categorical mathematics.

In recent decades, the early olfactory system (EOS) of the fruit fly has become a leading model for studying olfactory processing and associative memory, owing in part to a well-characterized feedforward pathway that feeds the processes underlying associative memory and by examining the role played by a handful of neurons and synapses. The recent completion of dense electron-microscopy connectomes provides high quality visualizations of every cell type, neuron, and synapse along the early olfactory pathway. Yet a wiring diagram, however complete, does not by itself reveal the functional logic of a neural circuit. Reviewing the EOS connectome and synaptome datasets of the past fifteen years, we note that the feedforward pathway is embedded in dense local feedback circuits of large scale multi-input multi-output neurons. A systematic understanding of feedback loop abstractions, and their capacity to govern the input/output transformations at each neuropil stage, is the underlying foundation of the functional logic of the early olfactory circuits. In addition, we argue that a quantitative account of the functional logic requires an explicit model of the odorants present in the natural environment. Consisting of odorant objects, such a model defines the semantics and syntax of olfactory information processing, and calls for new distance measures for classifying the odorant semantics in support of associative memory operations. Furthermore, odor information processing must abide by causality, treating the circuit as a real-time, stage-by-stage cascade of giant local feedback loops.

Responses in the sensory thalamic nuclei are modulated by perceptual tasks. Whether such response modulations rely on feedback from cerebral cortex in humans is unknown. Here, we addressed this question in the context of visual speech recognition: the visual sensory thalamus, i.e. the lateral geniculate nucleus (LGN), has differential BOLD-responses to visual speech than non-speech control tasks. We tested whether such response modulation relies on the function of the visual association cortex, specifically visual-motion area V5/MT. We applied inhibitory transcranial magnetic stimulation (TMS) over bilateral visual-motion sensitive areas V5/MT on 26 healthy adults. Subsequently, participants performed a visual speech and a colour recognition task on identical muted videos of speaking faces during functional magnetic resonance imaging (fMRI). The LGN showed a significant signal change between the visual speech task and the colour task following Vertex stimulation as active control region. This modulation was significantly reduced following inhibitory V5/MT stimulation. V5/MT stimulation also reduced task-dependent functional connectivity between V5/MT and the LGN. These results identify corticothalamic feedback as integral mechanism in visual processing. In particular, the visual association cortex has a causal role in modulating LGN responses during speech recognition.

Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94–99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.

The emergence of organized spatiotemporal patterns is ubiquitous in oscillatory systems, from neural populations to engineered networks. Identifying these patterns and tracking how they evolve over time remains challenging, particularly when systems exhibit transient dynamics. Here, we introduce a framework based on spatial ordinal patterns to characterize the spatiotemporal dynamics of oscillatory systems. Our approach acts directly on the phase rather than the amplitude, with additional patterns introduced to account for near-equal phases. This symbolic representation encodes local spatial ordering relations, capturing both phase gradients and synchronized clusters within a single framework. From this construction, we define a spatial permutation entropy that quantifies the diversity of spatiotemporal patterns at each point in time, enabling the detection of transient dynamics and regime transitions as they occur. We show that this approach distinguishes phase-locked states with identical levels of global synchronization but distinct spatial organization and also characterizes partially synchronized states. We demonstrate the method on synthetic oscillator networks across multiple spatiotemporal regimes, and on resting-state EEG recordings from human volunteers, where it distinguishes different conditions within individual volunteers.

Componentwise weak convergence of signed synaptic kernels does not, by itself, determine the fast-synapse limit of a sparse threshold-reset network. Within a causal event protocol with clamped refractoriness and smooth positive-delay kernels, we construct two families whose excitatory and inhibitory measures converge weakly to $\delta_0$ while their microscopic arrival orders are reversed. A target fires in the excitatory-first family and not in the inhibitory-first family precisely when $x+a-b<\theta\le x+a$. Strict margins preserve this response under perturbations of the target state, aggregate E/I pulse masses, and bounded drift. The macroscopic effect persists on a moderately sparse Dale-compatible random block graph with $q_N\to\infty$ and $q_N/N\to0$. The two systems share their graph and initial data. Along every deterministic joint scale $\varepsilon_N\downarrow0$, their population-averaged firing counts differ by $1/2+o_{L^1}(1)$. A bounded-degree construction and a later probe show that the discrepancy is macroscopic and can persist through reset. Fixed positive-delay kernels with finitely many classes admit a stable regime. Before grazing, typewise-mixing sparse networks converge to a delayed class mean-field system. Directed Erdos-Renyi graphs yield the bound $O_P(\lambda_N^{-1/2}+\|\pi_N-\pi\|_1)$ when $\lambda_N\to\infty$ and $\lambda_N/N\to0$. This separates stable averaging at a fixed delay from singular collapse. In the latter, componentwise weak convergence discards signed arrival-order information needed by the threshold-reset response.

Lifelong learning requires acquiring new knowledge without erasing the old. Yet neural population codes for familiar stimuli and behaviors change over days and weeks. This coexistence of stable memory and changing internal codes may depend on how a learning system prevents forgetting. We therefore tested whether different continual-learning mechanisms produce distinct patterns of representational drift. We trained convolutional networks on sequential image classification tasks and recurrent networks on sequences of cognitive tasks, tracking fixed probe representations across learning. Experience replay preserved earlier tasks in both architectures while representations drifted progressively with the number of intervening tasks. Drift was structured: later visual-processing stages and recurrent units' temporal tuning were especially labile, whereas coarse class organization and task-relevant temporal structure persisted. In contrast, algorithm that strongly anchored weights nearly froze representations. Directly anchoring an old representation during replay likewise suppressed drift and impaired acquisition of subsequent tasks. Together, these results link representational drift to the stability–plasticity trade-off: its magnitude is shaped by the mechanism that protects old knowledge, and suppressing it can restrict future learning. Drift may therefore provide an observable signature of the constraints that enable continual learning in brains and machines.

In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning. To bridge this gap, we propose the Multi-feature Riemannian Hypergraph (MRieHy), a framework tailored for online test-time adaptation in MI-BCI decoding that leverages Riemannian geometry to strengthen cross-day transferability. MRieHy first computes Riemannian means of covariance matrices from cross-day training data to align multi-day distributions. It then constructs a hypergraph over covariance matrices using Riemannian distance, complemented by a second hypergraph over deep features built with cosine similarity. The two hypergraphs are fused via adaptively learned combination weights, jointly optimized with the label projection matrices. During online testing, MRieHy maintains a first-in-first-out buffer of recent samples, performs Riemannian alignment on the buffered data, and decodes with the learned hypergraph. Extensive experiments on a private four-class ECoG dataset and two public four-class EEG datasets validate that MRieHy achieves notable performance gains over state-of-the-art baselines.

Global workspace theory explains conscious access as the broadcasting of selected information to the rest of the network, but it lacks a formal criterion for identifying the mechanism that enables this access. We propose that a global workspace is a mediator, namely, a subnetwork that receives activity from distributed systems, transforms it through internal modes, and returns differentiated effects to the broader network. We formalize this claim as the Global Mediation Workspace (GMW), a control-theoretic formulation in which a candidate subnetwork is treated as an open system embedded in the remainder of the network. In this framework, reachability characterizes how the remainder can drive the candidate, observability characterizes how candidate states affect the remainder, and a boundary Hankel operator identifies the internal modes linking the two directions. The resulting signature quantifies mediation capacity, input-output alignment, effective dimensionality, and routed source-target breadth, each of which characterizes different components of global workspace. In synthetic benchmarks, we tested whether the signature can distinguish a planted differentiated mediator from dense hubs, one-sided receivers or broadcasters, and a split read/write aggregate with no common internal route. A nonlinear extension characterizes mediation through trajectory-conditioned differential operators, finite-amplitude response profiles, and state-dependent coalitions. As a preliminary application, we estimated the signature from ECoG recordings in four macaques under ketamine anesthesia. We found that input-output alignment was reduced during unconsciousness whereas potential capacity was increased. The GMW thus provides a formal and testable criterion for locating candidate global workspaces in neural recordings and for asking which aspects of mediation is crucial for conscious access.

Anticipated synchronization (AS), in which the receiver leads the sender and the phase lag is negative, can emerge in unidirectionally coupled dynamical systems when the receiver has faster internal dynamics than the sender. In cortical-like population models, AS and bistability between AS and delayed synchronization (DS) have been reported mainly in unidirectional motifs and have been proposed as possible explanations for phase relations observed in electrophysiological recordings. Because cortical areas are often connected bidirectionally, it is important to understand how excitatory feedback from the receiver to the sender affects the emergence and persistence of anticipatory synchronization and phase-bistable regimes. Here, we investigate the effect of the excitatory feedback on the phase relations between two cortical-like neuronal populations. We show that AS and phase bistability are not restricted to strictly unidirectional architectures, but remain robust in the presence of reciprocal coupling. In addition, we find that the transition from AS to DS can occur through different routes within the same motif, either via a bistable regime or via zero-lag synchronization, depending on the inhibitory coupling. More generally, the model exhibits a rich repertoire of phase relations, including: positive, negative, and zero-lag phase-lockings, as well as phase bistability, and phase-drift regimes. These results are consistent with the diversity of phase relations reported in electrophysiological experiments and suggest that fixed structural connectivity may support rapid reconfiguration of functional dynamics without requiring structural rewiring.