Quantitative Biology (Neurons and Cognition)@BioPapers
1h
Path-dependent triage: Early judgments lead conversational AI chatbots to miss mental-health emergencies

Veith Weilnhammer, Raymond Dolan, Lennart Luettgau, Virginia Corno, Elise Wilkinson, Viknesh Sounderajah, Hannah Richardson, …
arxiv.org/abs/2611.09647 [𝚚-πš‹πš’πš˜.𝙽𝙲]
People increasingly bring mental-health concerns to consumer AI chatbots. In these conversations, clinically relevant information rarely arrives all at once. Instead, it unfolds over multiple turns, often in response to the questions the chatbot asks. Recognizing risk therefore depends on how a chatbot integrates newly disclosed information (inference) and on the questions it asks on each turn to elicit this information (context-seeking). Here we evaluated the conversational triage performance of 13 frontier AI chatbots tasked with engaging in 10-turn conversations with simulated users. Simulated users were grounded in one of 112 structured mental-health case files, each with an associated clinician-validated urgency level, from routine care to emergency. Chatbots failed to recognize one in five mental-health emergencies (20.3%), nearly four times the failure rate when all relevant clinical details were presented in a single message (5.6%). This gap was not explained by missing informa
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45
Quantitative Biology (Neurons and Cognition)@BioPapers
2h
Spiking neuromodulation: a linear control perspective

Fulvio Forni, Rodolphe Sepulchre
arxiv.org/abs/2611.08571 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚎𝚎𝚜𝚜.πš‚πšˆ πš–πšŠπšπš‘.𝙾𝙲]
πŸ’¬Submitted to European Control Conference ECC 2027
Neuronal excitability is a nonlinear phenomenon, and the complexity of spiking experimental traces sometimes looks defeating. The present paper takes the view point that excitability is a neuromodulated property, and aims to demonstrate that the neuromodulation of spiking is amenable to the classical linear analysis tools of control theory. We illustrate the value of linear feedback analysis as an alternative to bifurcation analysis in the simple yet classical example of spiking modulation by the A-type potassium current.
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64
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 6
Reversible Dynamics, Asymmetric Actions: Parking as a Trajectory-Level Affordance

Adam Y. Shavit
arxiv.org/abs/2611.07965 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.πšπ™Ύ πš–πšŠπšπš‘.𝙾𝙲]
Drivers are often told to back into a tight parking space, not drive in nose first. The advice looks paradoxical, because a slow car is dynamically reversible: reverse its speed and replay the steering backward, and it retraces a feasible path. Played backward, though, reverse-in shows a car driving forward out of the space. Reverse-in and forward-in are different tasks: they share a start position but end facing opposite ways. We separate three clearances between car and obstacles. Static fit F is the clearance the parked car keeps at its best acceptable destination pose. Eventual access A∞ is the clearance it can keep all the way there with unlimited corrections. Resource-bounded access A_K is the clearance that survives a budget of K reversals, switches between forward and reverse. They satisfy Fβ‰₯ A∞β‰₯ A_K. In one fully specified scene (one car, bay, aisle and start), a certified argument proves A∞=F=0.5000 m for both tasks. In this scene, all certified forward/reverse asymmetry ther
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96
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 6
Self-Supervised Learning of Local Predictive Directions as a Neural Computation

Marco Zenari, Xuehao Ding, Daniel Burnham, Austin R. Ellis-Mohr, Anirvan M. Sengupta, Dmitri B. Chklovskii
arxiv.org/abs/2611.07809 [𝚚-πš‹πš’πš˜.𝙽𝙲]
Natural sensory dynamics are globally nonlinear but often exhibit simple local geometry, with nearby trajectories evolving preferentially along a small number of predictive directions. We ask how neural circuits could discover this geometry directly from temporal experience. We introduce Local Predictive Directions (LPD), a self-supervised framework that learns local directions whose projections maximally predict near-future trajectory evolution. LPD applies past-future canonical correlation analysis to temporal differences, making the learned representation invariant to translations of the underlying state while avoiding estimation of equilibria or a global dynamical model. To represent systems with multiple predictive regimes, we introduce a non-negative similarity-matching objective, with offline and online algorithms, that partitions trajectory segments into predictive domains, each associated with a distinct local predictive direction. The online algorithm admits a natural neural-
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75
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 6
From Connection Loss to Collision: Closed-Loop Fault Assessment of a Fly-Connectome SNN

Shuchao Gao
arxiv.org/abs/2611.06940 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.𝙽𝙴]
The public release of the complete adult fruit-fly brain connectome enables brain-scale spiking-network studies, motivating closed-loop assessment of connection loss. We test a connectome-derived controller in synthetic pursuit-escape and check record consistency. Across 576 test episodes in paired campaigns, the baseline mapping at 50% connection-row deletion yields 11/48 proportional and 0/48 relay collisions by 1 s, despite paired P/R trigger/burst schedules. The prespecified secondary relay-minus-proportional net-loss difference is -22.9 percentage points (95% seed-cluster CI [-33.3, -12.5]). Two separately recalibrated mappings yield smaller, uncertain point estimates; unequal healthy clearance, recalibration and different test cohorts limit attribution. A frozen command-rate warning detects only 2/13 baseline collisions across all tested deletion levels. In a separate author-designed fly/delayed-tank catalogue, checks plus model-specific replay detect 81/87 corruptions versus 36/
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343
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 5
Learning Time-Varying Connectomes for Stable Behavior Across Development

Christof Fehrman, Jacob J. Morra, Eva A. Naumann
arxiv.org/abs/2611.06895 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.𝙽𝙴]
Neural circuits must maintain functionality to drive effective behavior while their size, topology, geometry, and intrinsic dynamics change across development. To model developing visuomotor circuits and behavior, we investigated whether a single compact generative encoding could produce functional recurrent controllers across such changes. Building on the HyperNEAT algorithm, we represented this program as a compositional pattern-producing network that received developmental time as an input to generate age-specific connectivity. The developmental substrate prescribed neuronal birth, position, sensory organization, and intrinsic time constants. We tested this approach using two geometrically distinct developmental substrates and two visuomotor tasks: optomotor responses and phototaxis. Controllers optimized at only the initial or mature substrate generally failed when evaluated at other ages. In contrast, controllers optimized across multiple developmental snapshots maintained substan
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72
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 5
Synchronized Hearts, Desynchronized Brains: A Nonlinear Dynamics Perspective for Function and Failure

Irene Pellini, Simon Bauer, Seba Contreras, Ruben FernΓ‘ndez Busnadiego, Anushka Deb, Sophia Hartmann, Sayedeh Hussaini, …
arxiv.org/abs/2611.06618 [𝚚-πš‹πš’πš˜.𝙽𝙲]
Both the heart and the brain are built from electrically excitable cells, yet the emergent dynamics of these organs could not differ more. In the myocardium, a coherent traveling wave of action potentials ensures coordinated contraction; in the cerebral cortex, distributed, irregular spiking underlies efficient information processing and the generation of behavior. Here, we illuminate the principles that underlie the emergence of such distinct nonlinear dynamics using concepts from complex systems. We trace these principles across scales from ion channels and cellular action potential generation, through cellular coupling mechanisms and network architecture, to organ level dynamics and clinical phenotypes. Designed to support either synchronized waves or desynchronized information processing, the heart and the brain can thus be understood as operating on opposite sides of a synchronization phase transition. Crossing this transition implies pathological dynamics in both organs: Loss of
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6
120
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 5
Mind Control of LLMs via Closed-Loop Stimulation: Out-of-Distribution Robustness

Kasra Fallah, Rudramani Singha, Hannah Ghanei, Paul Sajda, James Anderson, Erfan Zabeh
arxiv.org/abs/2611.06418 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.𝙰𝙸 𝚎𝚎𝚜𝚜.πš‚πšˆ]
Brains and large language models need not be considered equivalent systems to reveal an interpretable commonality: both exhibit high-dimensional, nonlinear dynamics whose behavior can only be modeled imperfectly. This creates a shared challenge whenever we seek to intervene in their dynamics. A controller may either rely on a model identified around a particular operating regime and risk failure as the system moves away from that regime, or explicitly account for uncertainty and a changing internal state. Neurostimulation has confronted an analogous problem for decades. The limitations of fixed, open-loop stimulation have motivated closed-loop and state-dependent approaches that condition intervention on the ongoing state of the neural system. Inspired by this principle, we formulate LLM steering as a feedback-control problem in which the network's internal state, rather than a fixed intervention alone, determines how the system is perturbed. We further account for the mismatch between
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92
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 5
A minimal E/I circuit model explains differential recovery from amblyopia under TTX and reverse occlusion

Lawrence Udeigwe
arxiv.org/abs/2611.06189 [𝚚-πš‹πš’πš˜.𝙽𝙲]
πŸ’¬Submitted to PLOS Computational Biology
Recovery from amblyopia beyond the critical period remains a major challenge in visual neuroscience. Experimental studies show that transient inactivation of the fellow eye with tetrodotoxin (TTX) produces stronger and more persistent recovery than reverse occlusion (RO), despite both interventions reducing competition from the fellow eye. The circuit mechanisms underlying this difference remain unclear. We develop a minimal cortical circuit model combining excitatory-inhibitory population dynamics, BCM-like metaplasticity, homeostatic regulation, and eye-specific synaptic plasticity. Monocular deprivation drives the network into an amblyopic operating regime with reduced excitatory activity and fellow-eye dominance. Phase-plane and operating-regime analyses show that TTX and RO subsequently place the circuit in distinct dynamical states. TTX lowers inhibitory activity while maintaining excitatory activity above the BCM modification threshold, creating a larger plasticity-permissive re
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68
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 4
When Model Complexity Matters in Cross-Subject Naturalistic fMRI Prediction

Junhao Zhao, Jacob Kang, Colin Kurniawan, David Simberg, Nan Xu
arxiv.org/abs/2611.04041 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.𝙰𝙸]
Increasingly complex neural models predict blood-oxygen-level-dependent (BOLD) responses to naturalistic stimuli, but the predictive value of this added complexity remains unclear. We ask when pretrained stimulus representations help and how much downstream complexity is needed, with and without recent measured BOLD history. We compare linear diagnostic references and nonlinear predictors across HCP movie fMRI (154 subjects), BOLD Moments (10 subjects), and Algonauts 2025 (4 subjects). Under a shared offline protocol, models are fitted on training subjects and evaluated on held-out subjects viewing largely familiar stimuli, without refitting. Pretrained visual features generally improve prediction over raw pixels; for linear autoregression, this advantage becomes more pronounced over longer rollouts (recursive predictions without additional measured BOLD). Measured BOLD history provides substantial gains that generally diminish as rollouts lengthen. Nonlinear stimulus-only predictors c
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3
136
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 4
Fly Whiskers Meet LiDAR: Robust Navigation under Sensor Failure

Ali Mahzarnia, Homayoun Jamshidi
arxiv.org/abs/2611.03897 [𝚚-πš‹πš’πš˜.𝙽𝙲]
We extend FLYNN, a leaky integrator recurrent network wired on the Drosophila melanogaster connectome, with a 32-ray 2D LiDAR interface that requires no learned fusion layer beyond a scalar input gain. A 270 degree frontal scan at 8 m range is injected as inverted proximity into the 37 BM Vib head-bristle neurons. Across 24 paired comparisons in a four- state vision matrix with LiDAR on or off, FLYNN-LiDAR has 17 significant wins, 5 nonsignificant comparisons, and 2 significant losses against three LiDAR-capable baselines. Both losses are to MobileNet with the left eye off. With full vision, FLYNN- LiDAR reaches 96.6% success. Under total vision loss, the scan raises success from 45.2% to 64.6%; the baselines reach 44.6% to 56.6% with the scan on. Under out of distribution textures, it is the only LiDAR-capable model whose success improves with the scan (44.2% to 58.4%); both CNNs score 0%, and SmallWorld declines from 26.8% to 7.4%. Under periodic vision dropout, FLYNN-LiDAR achieves
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39
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 4
Evolved Spiking Neural Networks for EEG Seizure Detection: Calcium Dynamics and Delay Mechanics

Tuba Aksoy
arxiv.org/abs/2611.03841 [𝚚-πš‹πš’πš˜.𝙽𝙲]
Neuroevolution can discover compact network designs that are hard to specify by hand. The EONS framework evolves the topology, weights, thresholds and delays of neuromorphic spiking networks without gradients. We tested whether giving integrate-and-fire (IF) neurons an activity-dependent calcium current (Ca-IF neurons) improves evolved networks for EEG seizure detection, with calcium parameters fixed, shared across a network, or evolved for each neuron. On 10 held-out CHB-MIT patients never seen in training, per-neuron calcium raised the record-level Matthews correlation coefficient (MCC) from 0.592 to 0.726 over 20 independent runs (p = 0.008) and lowered the false-alarm rate from 0.17 to 0.10. Fixed or per-network calcium gave no significant benefit. IF networks with evolved synaptic delays of 1-3 ticks gave a similar gain (p = 0.005), whereas axonal delays did not. On Siena, with unchanged settings, per-neuron calcium gained +0.096 MCC over IF in one patient-disjoint split (p = 0.01
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145
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 3
Intrinsic network dynamics catalyze switching in functional connectivity states of the brain

Steven S. Kang, Dae-Jin Kim
arxiv.org/abs/2611.03071 [𝚚-πš‹πš’πš˜.𝙽𝙲]
Dynamic functional connectivity (dFC) reflects ongoing reconfiguration of large-scale brain networks, but the temporal organization and network processes underlying spontaneous state transitions remain poorly understood. We developed functional connectivity state switching (FCSS), a phase-coherence-based framework that tracks multiscale changes in dominant whole-brain connectivity configurations. In Human Connectome Project fMRI, FCSS was strongly evoked by cognitive state and condition switches, and distinct transition dynamics differentially predicted working-memory performance. During rest, spontaneous FCSS was characterized by progressive changes in network segregation, integration, and cross-network communication as connectivity approached a switching event. Critically, event-related analysis time-locked to spontaneous FCSS revealed opposing pre-switch dynamics: salience-network contribution increased while default-mode-network contribution decreased, suggesting coordinated engage
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135
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 3
Modeling Visual Cortex with Layerwise Multiscale Manifold Capacity Maximization

Thomas Yerxa, SueYeon Chung, Eero P. Simoncelli
arxiv.org/abs/2611.02245 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.π™²πš…]
Task-optimized deep neural networks have risen to prominence as the most predictive phenomenological models of responses in primate visual cortex, but leave much to be desired from the perspective of biological plausibility. One such limitation is the reliance on precise credit assignment through global backpropagation of error signals. Recent work has shown that this weakness can be circumvented by requiring each subsequent stage to solve a distinct and increasingly complex task, allowing for layerwise local learning signals. We propose a novel strategy for crafting such intermediate losses that uses an efficient coding framework formulated in terms of manifold capacity, and which can be computed using a sequence of canonical cortical computations. In particular, we leverage the relationship between the multiscale nature of visual signals and the dilation of receptive field sizes in cascaded visual representations to modulate complexity, allowing for the reapplication of these common
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114
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 3
Do agents need to sleep?

Ido Haber, Giulio Tononi
arxiv.org/abs/2611.02216 [𝚚-πš‹πš’πš˜.𝙽𝙲]
LLM-based agents, which increasingly operate persistently across sessions and accumulate memory through interaction, already exhibit the familiar symptoms of unchecked plasticity: semantic drift, degraded retrieval precision, propagation of stored errors, and saturation of memory capacity. Drawing on the selectionist "ringing, resonance and renormalization" (RRR) account of sleep, we propose that each of its three phases maps onto an operation feasible in digital memory architectures. Engineers building stateful agents, meanwhile, are independently rediscovering the problems sleep evolved to solve: what to forget, and when to do the forgetting. From this we derive the falsifiable prediction that selectionist consolidation will outperform instructionist alternatives as memory stores grow large. The core claim concerns the nature of continuous learning itself: plasticity has a price, and for any system that learns continuously under finite capacity while coupled to its environment, that
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83
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 3
A Planar Switching Neuron Shares Logarithmic First-Spike Latency with Its Three-Dimensional Parent But Not Its Onset Mechanism

Giorgio Sonnino, Vadim Shlyonskiy, Thomas Erneux, David Gall
arxiv.org/abs/2611.01632 [𝚚-πš‹πš’πš˜.𝙽𝙲 πš–πšŠπšπš‘-πš™πš‘]
Can a reduced neuron model reproduce first-spike latency scaling while changing how a stimulus initiates sustained firing? We compare a piecewise-linear planar Neurosimilator with its three-dimensional parent. Both exhibit logarithmic latency dependence over the investigated input intervals, with different fitted coefficients. The mechanisms differ. In the parent, sustained spiking and a stable resting focus coexist in a spiking–rest bistability regime. An abrupt step accesses spiking, while slow stimulation can retain rest at the same final input. Its step-induced firing threshold is therefore an operational basin-access threshold below the saddle-node limit point, not a loss of equilibrium stability. In the planar model, the resting equilibrium instead reaches a switching boundary while its subthreshold eigenvalues remain negative. We derive the planar latency law from this boundary approach. The model generates repetitive excursions without an imposed reset and admits exact trajecto
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158
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 3
Geometric and algebraic inference in humans and neural networks

Jacob Russin, Joonhwa Kim, Ellie Pavlick, Michael J. Frank
arxiv.org/abs/2611.00387 [𝚚-πš‹πš’πš˜.𝙽𝙲 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙢]
πŸ’¬Code: github.com/jlrussin/geome…,
Humans possess a remarkable ability to draw inferences from limited data. While some inferences are made intuitively from past experience, others require online reasoning based on novel information given in context. Here, we formalize these strategies, which we term geometric and algebraic inference, and study their computational tradeoffs in humans and neural networks. The geometric strategy gradually shapes representational geometry to reflect underlying relational structure, making subsequent inference cheap. The algebraic strategy flexibly attends to and reasons over novel relations given in context, but costs additional computation at inference time. We identify opposing signatures of these two strategies in transitive inference, a domain widely studied across species, and characterize the conditions under which each is observed. Our framework predicts that the geometric strategy should be dominant when relations are familiar, whereas the algebraic should emerge to support de novo
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158
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 2
Hyperbolic embedding of higher-order simplicial networks

Martin Guillemaud, Vito Latora, Mario Chavez
arxiv.org/abs/2610.41684 [𝚚-πš‹πš’πš˜.𝙽𝙲 πš™πš‘πš’πšœπš’πšŒπšœ.𝚍𝚊𝚝𝚊-πšŠπš—]
Many biological interactions involve groups rather than pairs, as when several proteins assemble into a multi-protein complex. Simplicial complexes naturally capture these collective interactions. Existing hyperbolic embeddings represent network hierarchy and similarity through a few interpretable coordinates, but rely only on pairwise links: they cannot distinguish three units engaged in a common interaction from three units connected only in pairs. We introduce here the hyperbolic higher-order (HHO) embedding, which embeds a simplicial complex into the PoincarΓ© disk, while incorporating its group interactions. The hyperbolic area of the triangle spanned by three interacting nodes then measures how compactly their interaction is represented. Testson synthetic and real-world networks, including a co-authorship network and the protein interaction network of Escherichia coli, show that HHO reduces the hyperbolic area of triangles representing simplices relative to those obtained from pai
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78
Quantitative Biology (Neurons and Cognition)@BioPapers
Nov 2
Expression of White and Gray Matter Autism Functional Network Connectivity Signatures in Infancy

Qiang Li, Jingyu Liu, Zening Fu, Armin Iraji, …
arxiv.org/abs/2610.40839 [𝚚-πš‹πš’πš˜.𝙽𝙲]
πŸ’¬Submitted to the 2027 IEEE 24th International Symposium on Biomedical Imaging (ISBI)
Functional connectivity studies have provided valuable insights into altered brain organization in autism spectrum disorder (ASD). However, most functional connectivity studies focus exclusively on either gray matter (GM) regions or white matter (WM) tracts and examine specific age groups, rather than investigating their functional interactions across brain development. This limits our understanding of how GM-WM functional interactions evolve developmentally and may obscure important aspects of brain network organization in ASD. We combined integrated WM+GM functional network connectivity (FNC) with brain-wide risk scores (BRSs) to quantify ASD-associated pattern expression in ABIDE I/II and an independent infant cohort. We employed a NeuroMark framework with predefined WM and GM intrinsic connectivity networks templates, to estimate subject-level FNC. First, we identified two distinct patterns of altered WM+GM FNC in ASD: GM-related hypoconnectivity centered on the paralimbic network
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97
Quantitative Biology (Neurons and Cognition)@BioPapers
Oct 30
Recognition of auditory objects from continuous and ultra-sparse spike streams in neuromorphic devices

Leonardo E. S. Tavares, Kevin D. Sargent, Jon T. Sakata, Dezhe Z. Jin
arxiv.org/abs/2610.38564 [𝚚-πš‹πš’πš˜.𝙽𝙲]
Energy consumption is a major concern for modern artificial intelligence (AI) applications. The brain is thought to achieve energy-efficient computation through sparse coding schemes. By emulating the spiking dynamics of the brain, neuromorphic chips hold the potential to realize energy-efficient AI. In this work, we use birdsong to demonstrate that auditory object recognition can be achieved using ultra-sparse coding. Feature-detecting neurons tuned to short temporal features (∼37 ms), sampled from exemplar syllables, transform continuous auditory signals into streams of ultra-sparse spikes that mark the onsets and offsets of features within the signal. Spike patterns elicited by the exemplars are stored as templates. During recognition, template matching is performed using axonal delays between feature-detecting and template-detecting neurons. Recognition is further enhanced by an additional group of specialized binary neurons trained to discriminate between syllable categories with
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