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Quantitative Biology Neurons and Cognition

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Wednesday 14 October · still arriving

Neurons and Cognition

EEG Correlates of Verbal and Perceptual Reasoning

Gwyneth Rolph

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.
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Neurons and Cognition

Reciprocity coordinates a three-way trade-off in recurrent neural circuits

Xiaoyu Zhang, Mingyi Huang, Yixuan Cui et al. (+2 more)

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, and task-state separability: higher reciprocity lowers capacity, strengthens stability, and compresses separability. Whether it improves behaviour depends on whether the stability gain outweighs the lost separation between task states. Connectome and microcircuit datasets from five species showed intermediate reciprocity, while reciprocity varied substantially across adult Drosophila brain regions, providing compatible structural context and motivating circuit-specific functional tests.
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All authors Xiaoyu Zhang, Mingyi Huang, Yixuan Cui, Xin Lu, Yuguo Yu

Neurons and Cognition

How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity

Nima Dehghani

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-neuron or direct population model therefore establishes an effective description at a specified scale. It does not uniquely identify the finer computation that generated the observed relation. Which resolution is adequate depends on the observable and perturbation of interest.
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Tuesday 13 October · still arriving

Neurons and Cognition

Effects of cortisol time-scales in a model of emotion regulation

Michael Buccino, Joshua Ginart, Eva Kaslik et al. (+1 more)

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 current coupling. Introducing temporal memory reshapes the parameter region supporting oscillations and alters their persistence. Weak and strong Gamma kernels produce remarkably similar global dynamics, whereas the discrete-delay limit generates a substantially richer bifurcation structure, including quasiperiodic activity. Together, these results suggest that the dynamical effects of cortisol depend not only on the characteristic timescale of its action, but also on the shape of the temporal memory through which past neural activity influences the present. Different forms of temporal integration may provide a mechanism by which neural systems implement distinct functional dynamics, while changes in this integration could promote atypical or pathological rhythmic regimes.
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All authors Michael Buccino, Joshua Ginart, Eva Kaslik, Anca Radulescu

Neurons and Cognition

Simulating Alzheimer's amyloid-like pathology on a fly central nervous system connectome

Taeho Jo

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-$\beta$ 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 damage fell on poorly connected, random or highly connected neurons; these flies lost 20%, 35% and 48% of contacts. When the contacts lost were equalised, the three groups differed less; whether the remaining difference reflects where the damage fell could not be established. In 48 flies, restoring only the contacts that surviving neurons had lost recovered 62% of the whole-brain deficit, and restoring the removed neurons 39%. In flies that had lost neurons delivering the odour, these accounted for about half the deficit due to neuron loss. One neuron could decide an outcome: all 66 flies that lost the giant fibre on the stimulated side, which commands escape, had a giant fibre rate below 100 Hz, against 1 of 254 that kept it. Whole-brain activity revealed damage at milder severity than the learning index, because it changed much relative to its variation between trials.
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Neurons and Cognition · Cross-listed from cs.AI

Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference

St John Grimbly, Nicolas Kuske, Evert A. Boonstra et al. (+6 more)

An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that need or by increasing its preference for outcomes that relieve it. We compare these two interventions, separately and together, in a foraging gridworld with three lethal needs, and ask how each changes survival, perception and the body model the agent learns. Precision sharpens interoceptive posteriors and improves survival. Preference reweighting keeps bodily states further from their lethal boundaries and changes the states from which the agent learns, so its learned model covers fewer need levels, with lower quality across all levels but slightly higher quality on the levels it visits. An experiment on 72 further layouts does not establish the predicted preference benefit on difficult layouts, but both interventions improve average survival there and their interaction is positive. Survival alone therefore gives an incomplete account of how a model prioritises needs and learns about its body.
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All authors St John Grimbly, Nicolas Kuske, Evert A. Boonstra, Charel van Hoof, Rowan Hodson, Benjamin Rosman, Ryan Smith, Mark Solms, Jonathan P. Shock

2610.18617 · 13 Oct · Accepted at IWAI 2026, forthcoming in the Springer proceedings. 17 pages: the 13-page camera-ready paper, reproduced without alteration apart from one typographical fix, followed by 4 pages of supplementary material. Code: https://github.com/sgrimbly/interoceptive-prioritisation-iwai2026-snapshot

Abstract PDF $AGNT ×3

Neurons and Cognition

Sense-Making phase transition framework for Affect

Miguel Bosch, Laura Fanfarillo, Gustavo Diez et al. (+3 more)

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 landscapes of the model, we show how neutral, uncertain, polarized, and mixed-valence regimes arise as a function of contextual sensory information, learned historical evaluation, and externally supplied information. We further show that the resulting valence-arousal relation provides a theoretical basis for the upper-open V-shaped organization discussed in the affect literature, while also opening a natural route toward a dynamical description of affect and emotions.
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All authors Miguel Bosch, Laura Fanfarillo, Gustavo Diez, Victor Gómez Mayordomo, J. Ricardo Arias-Gonzalez, Belén Valenzuela

Neurons and Cognition

Rewiring of adult neural networks - learning from structural plasticity across multiple scales

Michael Fauth, Han Lu, Jannik Luboeinski et al. (+36 more)

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 has the potential for comprehensive neuroscientific insights that can guide the development of medical applications, artificial neural networks as well as neuromorphic computing.
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All authors Michael Fauth, Han Lu, Jannik Luboeinski, Johanna Senk, Simone Albani, Valentina Armuzza, Paolo Carloni, Richmond L. Crisostomo, Hermann Cuntz, Ulrich Egert, Eugenio F. Fornasiero, Tobias Gemmeke, Claus C. Hilgetag, Sabine M. Hölter, Patrick Inoue, Björn Kampa, James C. Knight, Susanne Kunkel, Thomas Nowotny, Samora Okujeni, Nicola Palomero-Gallagher, Mariia Popova, Petra Ritter, Giulia Rossetti, Michael Schirner, Marc Spehr, Leon Stefanovski, Gaia Tavosanis, Agata Witkowska, Felix Wolf, Willem Wybo, Sandra Diaz-Pier, Maximilian Lenz, Abigail Morrison, Florian Röhrbein, Stefan Rotter, Johannes Schemmel, Christian Tetzlaff, Andreas Vlachos

Neurons and Cognition

Rethinking World Models for Biosignals

Xianglong Bao, Bosong Huang, Xiaoda Wang et al. (+4 more)

World models promise to learn how systems evolve and respond to actions, enabling simulation, planning and decision-making. Bringing this promise to biosignals requires more than accurate prediction of physiological recordings. We examine existing biosignal world models through two requirements for intervention use: a genuine action and evidence of its causal effect. We identify three fundamental bottlenecks rooted in physiology: Temporal Latency, the variable delay between an action and its observable effect; System Openness, the influence of drivers beyond the modelled intervention; and Endogenous Dynamics, the system's ongoing evolution without that intervention. Drawing on published evidence, we outline corresponding research directions and propose an Intervention-Effect Validation Checklist to guide evaluation. The aim is to help the biosignal community realise the core promise of world models by learning the latent mechanisms that govern physiological evolution and responses to interventions.
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All authors Xianglong Bao, Bosong Huang, Xiaoda Wang, Keqi Han, Carl Yang, Ming Jin, Shirui Pan

Neurons and Cognition · Cross-listed from physics.soc-ph

Collective Benefits and Efficiency in Coupled Echo State Networks

Polyphony Bruna, Rick Dale, Michael J. Spivey et al. (+1 more)

Simple aggregation of independent responses ("wisdom of crowds") generally improves collective performance through statistical facilitation, but interaction among members can sometimes hinder group outcomes. However, under certain circumstances, coordination studies show that interacting groups can outperform their individual members and nominal groups, and interaction can also be more efficient than independence. We present a reservoir computing model to investigate how interaction can enhance joint performance in terms of accuracy and efficiency. Echo state networks were trained independently to identify Japanese speakers from vowel recordings and tested jointly with and without coupled feedback. Model pairs reproduced the dyadic advantage reported by Bahrami et al. (2010) with interaction via pooled-feedback, but not when independent responses were aggregated together. Model dynamics revealed that interaction was beneficial whereas aggregated outputs were prone to diverging from runaway feedback over time. In terms of efficiency, larger groups of smaller interacting models outperformed smaller groups of larger models while equating for total amount of group computation. Our results offer a formal framework for explaining when and why interaction enhances collective intelligence.
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All authors Polyphony Bruna, Rick Dale, Michael J. Spivey, Christopher T. Kello

Monday 12 October

Neurons and Cognition · Cross-listed from cs.AI

Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models

Hanbo Xie, Yue Zhao and Robert C. Wilson

Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral patterns rather than theory of mind (ToM): the ability to infer others' mental states, including beliefs and intentions. We introduce a benchmark and diagnostic framework for repeated hide-and-seek against algorithmic opponents with explicitly defined recursive belief updates. Their ground-truth beliefs and action probabilities let us distinguish adaptive play, action prediction, and belief tracking through live interaction, fixed-history evaluation, and controlled information interventions. With structured elicitation and behavioral supervised fine-tuning (SFT), we find improved play in several tested settings, but diagnostics reveal limitations in opponent prediction and belief tracking. We test whether supervising opponent action probabilities can address this predictive deficit in Qwen2.5-7B-Instruct. Predictions improve when the model is told the opponent's type and parameter settings, but become less accurate when it must infer the opponent from history without this information. Training on examples that also omit these details reduces the gap, although improvements vary across opponents. Is this limitation specific to smaller open-weight models, or does it also appear in a frontier model? GPT-5.4 also benefits from correct opponent beliefs: supplying them improves its predictions and choices, whereas deeper explicit belief reports and self-generated memory do not consistently improve forecasting from interaction history. This contrast suggests that using useful belief information is easier than inferring it autonomously, even for a frontier model. Together, our findings distinguish improved play and action prediction from reliable belief tracking, providing a framework for evaluating mentalizing and testing targeted improvements.
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Neurons and Cognition · Cross-listed from cs.AI

Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors

Farhad Pashakhanloo and Venkatesh N. Murthy

The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecules, whereas natural odors contain tens to hundreds of components, adding extra layer of complexity to percept formation. Here, we explore the relationship between molecular composition and perceived similarity in natural odor objects. We study this using a database of more than 1,200 volatile-compound-in-food mixtures containing more than 8,000 molecular components, and use large language models (LLMs) as a scalable surrogate for deriving mixture-level perceptual descriptions. We find that language-derived representations provide useful supervision for predicting held-out mixture similarities from molecular composition. The prediction was consistently strongest when perceptual targets were derived from chemosensory prompts than from other sensory prompts. Further, during the mixture-level training, we learned low-rank molecular embeddings that enabled better prediction of held-out mixture similarities than component-overlap metrics, even for mixture pairs with no shared molecules. Although no single-molecule labels were used during training, the learned molecular geometry showed significant alignment with the independent human odor-descriptor space. Finally, we explored the use of external molecular features in our mixture-similarity prediction task. These features showed modest zero-shot alignment with mixture-level perceptual similarity. They improved prediction under limited supervision but offered little additional predictive benefit with the full training set. Together, these results show that language-derived descriptions can provide scalable supervision for predicting the perceptual organization of complex natural mixtures, contributing to a quantitative understanding of how molecular composition gives rise to odor perception.
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Sunday 11 October

Neurons and Cognition

Simple model of neurogliovascular unit activity with noradrenaline leading force

Andrey Yu. Verisokin, Darya V. Verveyko, Alexey R. Brazhe et al. (+1 more)

In this work we present a model of a neuroglivascular unit associated with a neuronal ensemble. We combine calcium dynamics in the astrocyte, the cascade of synthesis of arachidonic acid and its derivatives, as well as the activity of the network of inhibitory and excitatory neurons with changes in local blood flow. We add equations to the model for extracellular neurotransmitters such as GABA, glutamate, and norepinephrine. We show that the resulting model can exhibit different types of behavior, and also shows a significant connection between the functioning of the neural network and its supporting components.
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All authors Andrey Yu. Verisokin, Darya V. Verveyko, Alexey R. Brazhe, Stanislav V. Lagosha

Saturday 10 October

Neurons and Cognition

Predicting Cognitive Tasks from Single-Channel EEG through Task-Semantic Alignment

Ryota Kanai

Studies of data scaling in electroencephalography (EEG) and related noninvasive recordings suggest that simple wearable devices could eventually reveal more about cognitive states. However, publicly available EEG data are difficult to combine. Different laboratories record different tasks, participants, and devices. Task-agnostic self-supervised learning can exploit such data, but representations learned only with EEG signals have not consistently produced feature representations that are directly effective for decoding the cognitive state. We therefore aimed to incorporate task information by embedding experimental instructions, stimuli and response demands in a common semantic space, and trained an EEG encoder to align signals from a single anterior–posterior differential channel with those task vectors. This framework supports zero-shot prediction without target-condition calibration and few-shot prediction using labeled samples. On a reserved ten-class evaluation, the highest observed zero-shot balanced accuracy was 30.13% (chance 10%). In a separate developmental few-shot experiment, balanced accuracy reached 34.47% using 16 labeled EEG examples per class from another participant, and 74.99% using examples from the test participant's own task recording. Our experiments suggest that training models on datasets with multiple tasks per participant appeared useful, whereas adding datasets containing only one or a few tasks sometimes reduced established performance despite broader task coverage. These exploratory results suggest that EEG datasets that repeat varied cognitive tasks and everyday activities within the same individuals across days, devices and independent studies would be particularly useful for training an EEG foundation model for predicting cognitive states.
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Neurons and Cognition · Cross-listed from stat.ME

Bayesian adaptive design for neural recruitment curves

Vishweshwar Tyagi, Saihari S. Dukkipati, Daniel Twili et al. (+4 more)

Nonlinear response models remain ubiquitous across disciplines with recruitment curves describing muscle response to neural stimulation. Conventional acquisition methods typically rely on static uniform sampling, requiring many stimuli, while adaptive threshold-hunting designs such as ML-PEST forfeit accurate characterization of the overall curve. We present a Bayesian adaptive design that uses posterior uncertainty over curve parameters to propose stimuli that maximize information gain across the full recruitment curve. We additionally implement a focused acquisition method that can target arbitrary subset of curve parameters, which we use to evaluate and interpret our design. In simulations, our design outperforms uniform sampling in recovering threshold while matching ML-PEST, and outperforms ML-PEST in predictive curve accuracy while matching uniform. It likewise matches our threshold-focused acquisition, suggesting our design doesn't trade off threshold estimation for recovering the curve. We further show data collected using our design yields greater power when detecting a change in threshold in intervention studies. Our design proposes the next stimulus within 5 seconds, making it feasible for transcranial magnetic stimulation (TMS) experiments. Finally, we validate our design in TMS experiments and provide a library for Python, called onMEP, for online acquisition of neural recruitment curves.
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All authors Vishweshwar Tyagi, Saihari S. Dukkipati, Daniel Twili, Jonas M. Mikhaeil, Andrew Gelman, Jason B. Carmel, James R. McIntosh

Neurons and Cognition

Full Lyapunov Spectra of Random Neural Networks, Part I: Finite-Time and Asymptotic Theory

Haoyu Albert Wang and Wei P. Dai

We develop a dynamical mean-field framework for the full finite-time and stationary asymptotic Lyapunov spectra of random recurrent neural networks. At finite observation time, an exact log-determinant representation of tangent-propagator singular values leads to a closed-contour matrix Dyson equation for the smoothed spectral tail. In the stationary setting, a cocycle representation yields an operator equation for the asymptotic cumulative spectrum. Both formulations retain complete local gain histories and the repeated action of quenched connectivity. Direct singular-value calculations and network QR spectra support the predictions in the regimes examined, with sampling uncertainty and numerical cutoffs assessed separately. The stationary equation also recovers the known maximal exponent, and controlled gain-history replay illustrates how temporal correlations redistribute instability. This work was carried out in collaboration with ChatGPT 5.6 Sol. Toward the end of preparation we became aware of David G. Clark's contemporaneous work, Lyapunov spectrum of random neural networks[arXiv:2610.12426], which reports overlapping mean-field results for the full Lyapunov spectrum using a cavity approach.
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Friday 9 October

Neurons and Cognition · Cross-listed from cs.NE

DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness

Daniel Szelogowski

Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplified by sleep state misperception in paradoxical insomnia and boundary instability in narcolepsy type 1. Here, we present DyssomniaNN: a multi-scale biophysical computational framework coupling Borbély's two-process regulation of sleep homeostasis and circadian drive with Saper's hypothalamic reciprocal flip-flop circuit and a recurrent corticothalamic leaky integrate-and-fire spiking neural network. We formalize five clinical cohorts across an identical 72-hour photoperiod: healthy control, circadian rhythm sleep-wake disorder (delayed-phase), chronic insomnia, paradoxical insomnia, and narcolepsy type 1. The synthetic local field potential converges closely with empirical human electroencephalography spectra from the PhysioNet Sleep-EDF database across all canonical frequency bands (spectral mean absolute error of 0.33–0.35). Furthermore, we introduce the Well-Rested Index to quantify restorative efficacy. Simulations reveal that sleep duration fails to predict restorative recovery: paradoxical insomnia achieves normal total sleep time (21.15 h) yet yields low restoration (37.6%) from unsuppressed cortical beta-band intrusion, whereas hypocretin deficiency in narcolepsy produces severe nocturnal fragmentation (781 min of wake after sleep onset) and 35 sleep-onset rapid eye movement periods. DyssomniaNN provides an in silico platform for mechanistic sleep research and virtual neuropharmacology.
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Neurons and Cognition

EEG Entrainment to Non-Speech Rhythmic Patterns Associated with Dimensions of Language Performance

Francisco J. Martínez-Murcia, Nicolás J. Gallego-Molina, Marco A. Formoso et al. (+1 more)

Developmental dyslexia (DLX) affects 5-12% of the global population and impairs learning to read. Its biological basis, particularly the difficulties observed in oral phonological tasks, remains debated. Entrainment, the coupling of brain oscillations to speech rhythms, is essential for encoding phonological representations. We investigated whether brain entrainment to non-speech rhythms could predict DLX in 48 participants listening to white noise amplitude-modulated at rates matching Spanish speech units. Phase-amplitude coupling (PAC) analysis of electroencephalography (EEG) signals yielded accurate DLX prediction for the 40 Hz stimulus using the Heights Ratio PAC measure (balanced accuracy 0.929, p = 0.0069, in the best case), indicating a stronger phonemic entrainment deficit in dyslexic individuals. Regression analysis in control (CTL) participants showed a strong dependence between reading performance scores and greater entrainment in language-associated brain regions. In particular, participants with poorer phonemic awareness, reading comprehension, or rapid automatized naming showed greater right-hemisphere entrainment to the 40 Hz stimulus. This finding adds evidence for the hemispheric compensatory mechanisms hypothesized in the literature. In conclusion, our methodology identifies dyslexia-specific entrainment patterns near language-associated brain regions, together with compensatory entrainment in right-hemisphere areas.
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All authors Francisco J. Martínez-Murcia, Nicolás J. Gallego-Molina, Marco A. Formoso, Andrés Ortiz

Neurons and Cognition

Beyond Anxiety and Avoidance: A Dynamic Feedback-Regulation Framework for Adult Romantic Attachment

Mateusz Pomianek

Adult attachment is commonly represented by attachment anxiety and avoidance, but these person-level dimensions do not by themselves specify how momentary relational conditions are perceived, compared with desired conditions, translated into action, and updated after interpersonal feedback. We present the Attachment Regulation and Control (ARC) framework as an integrative feedback-regulation account that makes these temporal assumptions explicit and empirically testable. In ARC, control refers to feedback-based regulation toward relational goals in the control-theoretic sense, not interpersonal dominance, coercion, or a generalized need for control. ARC does not claim a novel control-system architecture. Instead, it synthesizes established control-theoretic attachment ideas, secure-base representations, perceived partner responsiveness, relational discrepancy, dyadic regulation, and recovery into a parsimonious sequence for adult romantic relationships: perceived relational state, desired relational state, discrepancy, regulatory action, partner feedback, state updating, and recovery. The framework treats the discrepancy score itself as a falsifiable modeling restriction by comparing it with perceived-state-only, unconstrained perceived-plus-desired-state, and asymmetric discrepancy alternatives. Attachment anxiety and avoidance form core regulation moderators. Secure-base representation is a candidate requiring incremental prediction of recovery outcomes. Mentalized affectivity and regulation flexibility remain optional moderators, while adult disorganization is an independent extension outcome.
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Neurons and Cognition

Excitability across scales: a unifying EI model

Thomas S.J. Burger and Rodolphe Sepulchre

Neuronal excitability is commonly modeled using different principles at the cellular and population scales, making it difficult to study how microscopic mechanisms shape macroscopic activity. We introduce a spatiotemporal excitatory-inhibitory (EI) model that unifies these scales through a common mixed-feedback architecture. The model retains the conductance-based circuit structure of Hodgkin-Huxley models and describes intrinsic and synaptic currents through memductances whose temporal and spatial voltage dependence encodes dynamics at the relevant scale. In its minimal form, temporal excitability arises from fast excitatory feedback opposed by slower inhibitory feedback, while spatial excitability arises from short-range excitation opposed by longer-range inhibition, recovering the core lateral-inhibition motif of Amari neural fields. We show that combining these mechanisms gives rise to population excitability in strongly heterogeneous populations. In particular, the model generates population spikes that are localized in both space and time and possess population-level scales distinct from those of individual neuronal spikes, despite substantial cell-to-cell parameter variability. We then show that both intrinsic temporal excitability and spatial synaptic feedback are required for coherent mean-field events and robust spatiotemporal population rhythms. Finally, selective modulation of memductances in a subset of the population tunes the frequency of the population rhythm, illustrating how changes at the cellular scale can shape macroscopic dynamics. By separating circuit architecture from modeling scale, the proposed framework provides a physically interpretable approach to analyzing and designing neuronal excitability across scales.
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Neurons and Cognition

FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model

Aashish Dhawan

Connectome-constrained neural networks make biological components directly accessible to intervention, but their mechanistic conclusions can still depend on the comparison model, intervention, circuit representation and trained solution. We stress-test these dependencies in flyvis, a Drosophila motion-vision model, using its 50-model ensemble, task-matched causal CNN controls and replicated interventions. At the level of model behaviour, three-level reverse-phi reversal in flyvis is strongly period- and velocity-dependent and also appears in task-matched CNNs; neither family reproduces the fly's stationary-sawtooth response. Adversarial direction reversal requires a perturbation bound of 0.21–0.27 times stimulus contrast in flyvis versus 0.13–0.25 in CNNs, but the gap over a parameter-matched CNN largely closes for larger CNNs. At the level of mechanism, matching attribution to the intervention matters: for held-out-mean replacement, first-order edge attribution patching reaches pooled AUROC 0.970 and Spearman 0.872, versus 0.933 and 0.126 when its zero-aligned score is evaluated against the same ground truth. Circuit conclusions also vary with representation and ensemble member. Greedy cell-type sets meet a model-relative T4/T5 tuning criterion in 25 of 50 models, while T4/T5 output silencing reduces the tested optic-flow behaviour to at most two of eight clips in 40. In six models where the cell-type search first succeeds, the corresponding connection sets can be pruned to 13–31 added classes; under an additional two-sided response-amplitude check, only two of those six first-success cell-type sets still meet the tuning threshold. These results support a narrower use of connectome-constrained models: they make mechanistic hypotheses directly testable, but the resulting claims must be scoped to the model family, intervention, representation and trained solution.
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Thursday 8 October

Neurons and Cognition

Energy, Information, and the Limits of Neuropsychiatric Classification

Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi et al. (+15 more)

Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak neuroimaging separability remains unresolved. Drawing on statistical thermodynamics and the metabolic costs of neural coding, we propose that psychiatric disorders produce constrained deformations of a largely shared repertoire of brain activity patterns, limiting the class-specific information available for classification. Observations: We develop a framework linking physical resource constraints, landscape deformation, distributional separation, and attainable classification, and examine its components using human neuroimaging, synthetic data, and an exactly specified physical model. In resting-state fMRI from 1952 FOR2107 participants (823 with major depressive disorder; 1129 healthy controls), held-out energy-landscape estimates and nonlinear classifiers agreed across 13 comparisons (r = 0.996). Similar results emerged at both FOR2107 centers and in 900 SRPBS participants spanning multiple psychiatric diagnoses. In synthetic data, classification improved with distributional separation and approached the optimum. In an equilibrium three-well model, statistical energy equaled physical potential energy scaled by inverse temperature. Modest deformation redistributed state occupancy while optimal snapshot classification remained weak. A literature map found insufficiently comparable evidence to test the human metabolic constraint. Conclusions: Clinically consequential differences may arise within a largely shared neural repertoire. The thermodynamic framework connects physical deformation to classification under specified assumptions; its metabolic implementation in humans remains untested. Testing this mechanism requires joint measurements of brain activity, bodily physiology, and metabolism.
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All authors Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi, Sarah Alizadeh, Madleen Stenger, Amaia Zurinaga Gutierrez, Shakiba Bakhshian, José C. García Alanis, Nils R. Winter, FOR2107 Consortium, Frank Bremmer, Stefan G. Hofmann, Udo Dannlowski, Tilo Kircher, Sharmili Edwin Thanarajah, Igor Nenadić, Tim Hahn, Hamidreza Jamalabadi

Neurons and Cognition · Cross-listed from cond-mat.dis-nn

Lyapunov spectrum of random neural networks

David G. Clark

The Lyapunov spectrum of a nonlinear recurrent neural network with random asymmetric couplings is calculated in the limit $N\to\infty$. The calculation is based on a finite-$N$ identity that expresses the cumulative distribution of Lyapunov exponents as a response function of the tangent-space dynamics, with the tangent trajectory selected by a minimum-norm condition rather than by an initial condition. A cavity method then determines this response function at large $N$ through a self-consistent single-site problem. This result establishes that the chaos in this network is extensive and gives access to diffeomorphism-invariant properties of the dynamics. This work was done in collaboration with the AI models GPT-6 Astra and Claude Opus 5.5.
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Neurons and Cognition

Neural Decoding as Cognitive Inference

Yi Guo, Changhong Jing, Yong Hu et al. (+4 more)

The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observations remains a central problem in neural decoding. Existing neural decoding methods map neural observations to predefined external labels based on the stimulus-response principle, often capturing recording-specific spurious correlations. Inspired by how the brain infers the world, and specifically by Bayesian brain theory, we recast neural decoding as cognitive inference constrained by brain-intrinsic priors, yielding high-level meta-neural semantic representations. In decoding experiments spanning five neural recording modalities and three cognitive domains (motor, perception and internal mentation), our cognitive inference method reorganized the geometry of neural observation representations, yielding meta-neural semantic representations that exhibited consistent geometric relationships across cognitive tasks and enabled the recovery of stable cognitive states from variable neural observations. Our work provides an account of how the brain maintains relatively stable cognition despite continual changes in the external environment. Cognitive stability is sustained through cognitive inference from changing neural activity, without requiring fixed neural activity patterns.
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All authors Yi Guo, Changhong Jing, Yong Hu, Yan Liu, Michael K. P. Ng, Shanshan Wang, Shuqiang Wang

Neurons and Cognition · Cross-listed from q-bio.QM

Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz et al. (+4 more)

Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of translational information because it captures high-dimensional variation in nervous-system function that can be measured in both animals and humans. However, its high dimensionality makes it difficult to distinguish conserved disease-related features from variation arising from species, recording modality and experimental context. Here, we test whether shared neural dynamics can be identified directly from electrophysiology data by learning representations organized by biological state rather than species. We develop a dual-rule contrastive learning framework that aligns corresponding mouse and human states while preserving separation between distinct phenotypes. This framework recovered conserved sensory-response structure across species and, in epilepsy, resolved distinct relationships between three mouse models and heterogeneous human patient populations. When treated animals were projected into a frozen cross-species representation, drug-induced movement towards the human-aligned healthy state retrospectively tracked known clinical efficacy across ten model-drug combinations including a disease-specific detrimental effect. The framework also identified shared disease-associated neural dynamics between Fmr1-knockout mice and human 16p11.2 copy-number variant carriers despite differences in genetic aetiology and recording modality. Together, these findings show the potential of cross-species neural representation learning to map heterogeneous human disease onto experimentally tractable preclinical states and assess whether interventions restore human-relevant circuit function.
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All authors Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz, Jydell Ashley Palomo Penollar, Edmayelle Villavicencio Alforja, Jobi Fallaeria Subosa, Gabriel Ocana-Santero

Neurons and Cognition

If the brain were so simple: Information-based drivers of intelligence and their interaction through an n-body-inspired framework

Brett J. Kagan, Johnson Zhou, Forough Habibollahi et al. (+1 more)

Theories of how the brain processes information and returns intelligent outputs are numerous and often difficult to conclusively test. Here, we consider an approach grounded in informatics and algorithmic thermodynamics for how neural systems respond to different patterns of information in different ways, with a focus on the relationship between the information entropy of a signal and a new proposed quantity we term information enthalpy, which represents the internal resource of structured information within a system available to be used for predictions and other forms of work. To evaluate the capacity of the external signals to increase information enthalpy in the system, we introduce the information enthalpy potential (IEP) as a defined metric. We propose and implement a method for quantifying the amount of potential information enthalpy - the IEP - in a given signal across a range of example signals. We offer a conjecture of how information enthalpy may be treated by neural systems within a biological framework before showing how it may integrate and offer falsifiability to existing approaches such as the Free Energy Principle. This framework is also positioned in light of the role of neural criticality. Finally, we postulate a testable and falsifiable framework where the distinct mechanisms within each driver interact through a described n-body-inspired model to govern the "motion" of a neural system through a high dimensional state-space. Through these processes, it is proposed that these features form the most fundamental basis of the neural drivers that give rise to the complexity of adaptive behaviors that are commonly called: intelligence.
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All authors Brett J. Kagan, Johnson Zhou, Forough Habibollahi, Valentina Baccetti

Wednesday 7 October

Neurons and Cognition

Mean Field Theory Based on the Spike Time Response Curve for Synchronization Within and Between Two Alternating Populations of Neural Oscillators with Delays

Ananth Vedururu Srinivas and Carmen C. Canavier

In order to study oscillators coupled by pulses emitted at discrete times, it is generally assumed that the effect of each pulse is an instantaneous phase shift, with a possible conduction delay between emission and receipt. However, we are interested in neural oscillators in which each pulse (action potential) activates a biexponential synapse, and at high frequencies, the duration of the synaptic conductance may be greater than the network period. Therefore, the effect of received pulses can summate over the course of several cycles. To overcome this limitation, we previously defined a mean field approach for a single synchronous population. Here, we extend it to two alternating synchronous populations by assuming a perturbation of a single oscillator from one of the synchronous populations which is represented by a single self-connected neural oscillator. The train of delayed biexponential synapses emitted by each oscillator is divided into a tonic and a phasic component. Because of the continuous rather than pulsatile nature of the input, we had to drop the concept of oscillator phase and derive self-consistent criteria for the existence and stability of an alternating firing pattern between two synchronous populations based solely on the perturbation of time intervals from their steady state values. Simulations showed that for high frequency oscillations as defined above, the mean field approach predicts the existence and stability of phase-locking between two synchronous populations better than the instantaneous phase shift approach. This approach may generalize to other forms of coupling amongst non-neural oscillators.
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Neurons and Cognition · Cross-listed from cs.LG

Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

Rita Huan-Ting Peng and Nhat Bui

As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy alone. We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar. A lightweight CNN–Transformer EEG foundation model pretrained on the Temple University EEG Corpus (TUEG) extracts segment-level representations. Using the Temple University Epilepsy Corpus (TUEP), representations are mapped to a shared latent-state space, and sparse multinomial logistic transition distributions (mLTD) are fit independently to each subject to obtain personalized transition-dependency graphs $W_n$. Analyses include $n{=}198$ subjects (99 epilepsy / 99 non-epilepsy). At $k{=}4$, epilepsy subjects exhibit substantially denser learned dependency structure ($p{=}1.1\times10^{-7}$), with the same pattern at $k{=}6$ (19.90 vs. 13.46; $p{=}5.2\times10^{-5}$). Graph-derived features provide moderate group discrimination under 5-fold subject-wise cross-validation (AUROC 0.68 at $k{=}4$; 0.65 at $k{=}6$). In contrast, held-out log-likelihood is nearly identical between groups at $k{=}4$ ($-0.992$ vs. $-0.991$; $p{=}0.95$), with similarly matched next-state prediction (AUROC 0.855 vs. 0.861; $p{=}0.54$). Thus, similar predictive fit does not imply similar learned dynamics: groups can be comparably predictable while differing substantially in the internal dynamical structure learned by personalized models. This distinction motivates evaluating learned structure alongside predictive performance in personalized clinical models.
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Neurons and Cognition · Cross-listed from cs.LG

Learning infinite context windows in recurrent architectures via spatial neural computing

Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller et al. (+2 more)

Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant memory, yet they struggle to capture long-range dependencies due to vanishing gradients and limited receptive fields. To address these limitations, we introduce a second-order recurrent model in which the standard neuron-to-neuron communication is replaced by a spatially evolving field governed by (discretized) partial differential equations. Drawing inspiration from the role of cortical waves in brain computation, this mechanism allows structured spatiotemporal patterns to serve as an implicit, high-capacity memory. We show that the resulting model is equivalent to a structured infinite-order RNN in which the current state depends explicitly on its entire history of past states, yielding an effectively unbounded receptive field with a fixed number of parameters. We further derive constructive conditions to ensure marginal stability, constraining the gradient spectrum on the unit circle and thereby eliminating vanishing and exploding gradients. Empirically, the proposed architecture outperforms other recurrent models on long-horizon benchmarks while using substantially fewer parameters, demonstrating that spatial dynamics can effectively bridge the gap between efficient inference and long-term memory.
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All authors Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller, Adilson E. Motter, Jorge Cortés

Neurons and Cognition

Structure alone supports efficient visual computation in the Drosophila visual system

Eudald Correig-Fraga, Roger Guimerà and Marta Sales-Pardo

Understanding the extent to which measured synaptic wiring determines computation remains a central challenge. Here, we couple the proofread adult Drosophila melanogaster connectome to an anatomically faithful model of its eye. Visual information is inputted in the eye model, then passed to the connectome, and finally read from a Kenyon-cell-centered linear decoder. This creates a connectome-only model in which the anatomical graph and eye geometry are fixed and only scalar synaptic gains and neuronal thresholds may be learned. The model supports multitask vision, including color discrimination, shape classification, and numerical discrimination that follows a ratio-dependent scaling characteristic of approximate number perception. To test whether precise connectivity is consequential under wiring economy, we compare the biological graph to randomized ensembles that increasingly preserve biological synaptic constraints. At matched wiring cost, the biological network consistently yields higher accuracy, whereas less constrained rewiring surpasses it at the cost of inflated wiring. These findings indicate that the measured connectivity and eye geometry jointly set efficient operating points for visual computation.
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