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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Current models cannot account for V1’s specialisation for binocular natural image statistics

A long-standing observation about primary visual cortex (V1) is that the stimulus selectivity of neurons can be well explained with a cascade of linear computations followed by a nonlinear rectification stage. This framework remains highly influential in systems neuroscience and has also inspired recent efforts in artificial intelligence. The success of these models include describing the disparity-selectivity of binocular neurons in V1. Some aspects of real neuronal disparity responses are hard to explain with simple linear-nonlinear models, notably the attenuated response of real cells to \"anticorrelated\" stimuli which violate natural binocular image statistics. General linear-nonlinear models can account for this attenuation, but no one has yet tested whether they quantitatively match the response of real neurons. Here, we exhaustively test this framework using recently developed optimisation techniques. We show that many cells are very poorly characterised by even general linear-nonlinear models. Strikingly, the models can account for neuronal responses to unnatural anticorrelated stimuli as well as to most natural, correlated stimuli. However, the models fail to capture the particularly strong response to binocularly correlated stimuli at the preferred disparity of the cell. Thus, V1 neurons perform an amplification of responses to correlated stimuli which cannot be accounted for by a linear-nonlinear cascade. The implication is that even simple stimulus selectivity in V1 requires more complex computations than previously envisaged.\n\nSignificance statementA long-standing question in sensory systems neuroscience is whether the computations performed by neurons in primary visual cortex can be described by repeated elements of linear-nonlinear units (a linear filtering/pooling stage followed by a subsequent output nonlinearity, such as a squaring). This question goes back to the Nobel-prize winning work by Hubel & Wiesel who argued that orientation selectivity in V1 can qualitatively be explained in this way. In this paper, we show that V1 neurons have an amplification of their response to stimuli which are contrast matched in the two eyes, and that the recovered models cannot describe this property. We argue that this likely represents more sophisticated computations than can be compactly described by the linear-nonlinear cascade framework.

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bioRxiv2019

Sensory-motor brain dynamics reflect architectural affordances

Anticipating meaningful actions in the environment is an essential function of the brain. Such predictive mechanisms originate from the motor system and allow for inferring actions from environmental affordances, the potential to act within a specific environment. Using architecture, we provide a unique perspective to the abiding debate in cognitive neuroscience and philosophy on whether cognition depends on movement or is decoupled from our physical structure. To investigate cognitive processes associated with architectural affordances, we used a Mobile Brain/Body Imaging approach recording brain activity synchronized to head-mounted virtual reality. Participants perceived and acted upon virtual transitions ranging from non-passable to easily passable. We demonstrate that early sensory brain activity, upon revealing the environment and before actual movement, differed as a function of affordances. Additionally, movement through transitions was preceded by a motor-related negative component also depended on affordances. Our results suggest that potential actions afforded by an environment influence perception.\n\nSIGNIFICANCE STATEMENTBy using electroencephalography and virtual reality, our research provide a unique perspective to the centurial open-ended debate in cognitive neuroscience and philosophy on the relation between cognition, movement and environment. Our results indicate that cortical potentials vary as a function of bodily affordances reflected by the physical environment. Firstly, the results of this study implies that cognition is inherently related to potential movement of the body, thus we advance that action is interrelated with perception, actively influencing the perceivable environment. Secondly, as cortical potentials are influenced by the potential to move, which in turn is the task of architectural design, architects holds largely a privilege of human health, and thus potentially capable of provoking and preventing physiological conditions.

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bioRxiv2019

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing enormously in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually-curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of uncontrolled naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

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bioRxiv2019

Network-wide abnormalities explain memory variability in hippocampal amnesia

Patients with hippocampal amnesia play a central role in memory neuroscience but the neural underpinnings of amnesia are hotly debated. We hypothesized that focal hippocampal damage is associated with changes across the extended hippocampal system and that these, rather than hippocampal atrophy per se, would explain variability in memory between patients. We assessed this hypothesis in a uniquely large cohort of patients (n=38) after autoimmune limbic encephalitis, a syndrome associated with focal structural hippocampal pathology. These patients showed impaired recall, recognition and maintenance of new information, and remote autobiographical amnesia. Besides hippocampal atrophy, we observed correlatively reduced thalamic volume, resting-state inter-hippocampal connectivity and activity in posteromedial cortex. Associations of hippocampal volume with recall, recognition, and remote memory were fully mediated by wider network abnormalities, and were only direct in forgetting. Such network abnormalities may explain the variability across studies of amnesia and speak to theoretical debates in memory neuroscience.

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bioRxiv2019

Spectral Graph Theory of Brain Oscillations

The relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magneto-encephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. Significance StatementThe relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

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bioRxiv2019

A general spectral decomposition of causal influences applied to integrated information

Quantifying causal influences between elements of a system remains a central topic in many fields of research. In neuroscience, causal influences among neurons, quantified as integrated information, have been suggested to play a critical role in supporting subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences can reveal frequency-specific influences that are not observed in the time-domain. To date however, a spectral decomposition of integrated information has not been put forward. In this paper, we propose a spectral decomposition of integrated information in linear autoregressive processes. Our proposal is based on a general and flexible framework for deriving the spectral decompositions of causal influences in autoregressive processes. We show that the framework can retrieve the spectral decompositions of other well-known measures such as Granger causality. In simulation, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time-domain. We propose that the spectral decomposition of integrated information will be particularly useful when the underlying frequency-specific causal influences are masked in the time-domain. The proposed method opens the door for empirically investigating the relevance of integrated information to subjective conscious experience in a frequency-specific manner.\n\nAuthor summaryUnderstanding how different parts of the brain influence each other is fundamental to neuroscience. Integrated information measures overall causal influences in the brain and has been theorized to directly relate to subjective consciousness experience. For example, integrated information is predicted to be high during wakefulness and low during sleep or general anesthesia. At the same time, neural activity is characterized by well-known spectral signatures. For example, there is a prominent increase in low frequency power of neural activity during sleep and general anesthesia. Taking account of the spectral characteristics of neural activity, it is important to separately quantify integrated information at each frequency. In this paper, we propose a method for decomposing integrated information in the frequency domain. The proposed framework is general and can be used to derive the spectral decomposition of other well-known measures such as Granger causality. The spectral decomposition of integrated information we propose will allow empirically investigating the relationship between neural spectral signatures, integrated information and subjective consciousness experience.

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bioRxiv2019

A dynamical systems framework to uncover the drivers of large-scale cortical activity

A fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brains interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

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bioRxiv2019

An Algorithmic Barrier to Neural Circuit Understanding

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.

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bioRxiv2019

A Universal Nonparametric Event Detection Framework for Neuropixels Data

Neuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as \"change-points\". The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics in some brain regions display biologically interesting patterns that hint at functional pathways, as well as temporally-precise coordination with behavioral dynamics. We hypothesize that neural activities underlying spontaneous behavior, though distributed brainwide, show evidences for network modularity. Moreover, we envision the proposed framework to be a useful off-the-shelf analysis tool to the neuroscience community as new electrophysiological recording techniques continue to drive an explosive proliferation in the number and size of data sets.

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bioRxiv2019

Mathematics and the brain - a category theoretic approach to go beyond the neural correlates of consciousness

Consciousness is a central issue in cognitive neuroscience. To explain the relationship between consciousness and its neural correlates, various theories have been proposed. We still lack a formal framework that can address the nature of the relationship between consciousness and its physical substrates though. Here, we provide a novel mathematical framework of Category Theory (CT), in which we can define and study the \"sameness\" between \"different\" domains of phenomena such as consciousness and its neural substrates. CT was designed and developed to deal with the \"relationships\" between various domains of phenomena. We introduce three concepts of CT including (i) category; (ii) inclusion functor and expansion functor; and (iii) natural transformation between the functors. Each of these mathematical concepts is related to specific features in the neural correlates of consciousness (NCC). In this novel framework, we will examine two of the major theories of consciousness: integrated information theory (IIT) of consciousness and temporo-spatial theory of consciousness (TTC). These theories concern the structural relationships among structures of physical substrates and subjective experiences. The three CT-based concepts, introduced in this paper, unravel some basic issues in our search for the NCC; while addressing the same questions, we show that IIT and TTC provide different albeit complementary answers. Importantly, our account suggests that we need to go beyond a traditional concept of NCC including both content-specific and full NCC. We need to shift our focus from the relationship between \"one\" neuronal and \"one\" phenomenal state to the relationship between a structure of neural states and a structure of phenomenal states. We conclude that CT unravels and highlights basic questions about the NCC in general which needs to be met and addressed by any future neuroscientific theory of consciousness.\n\nAuthor summaryNeuroscience has made considerable progress in uncovering the neural correlates of consciousness (NCC). At the same time, recent studies demonstrated the complexity of the neuronal mechanisms underlying consciousness. To make further progress in the neuroscience of consciousness, we need proper mathematical formalization of the neuronal mechanisms potentially underlying consciousness. Providing a first tentative attempt, our paper addresses both by (i) pointing out the specific problems of and proposing a new approach to go beyond the traditional approach of the neural correlates of consciousness, and (ii) by recruiting a recently popular mathematical formalization, category theory (CT). With CT, we provide mathematical formalization of the broader neural correlates of consciousness by its application to two of the major theories, integrated information theory (IIT) and temporo-spatial theory of consciousness (TTC). Together, our CT-based mathematical formalization of the neural correlates of consciousness including its specification in the terms of IIT and TTC allows to go beyond the current concept of NCC in both mathematical and neural terms.

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bioRxiv2019

Detecting task-dependent modulation of spatiotemporal module via tensor decomposition: application to kinematics and EMG data for walking and running at various speed

How the central nervous system (CNS) controls many joints and muscles is a fundamental question in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. Another fundamental question is how the CNS generates numerous repertories of movement patterns. One hypothesis is that the CNS modulates the spatial and/or temporal modules depending on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and the task-dependent modulation of those modules. Although previous methods attempted to quantify these aspects, they considered the modulation in only the spatial or temporal module. These limitations were possibly due to the constraints inherent to conventional methods for quantifying the spatial and temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the spatial module, the temporal module, and the task-dependent modulation of these modules without such limitations. We further demonstrate that the tensor decomposition provides a new perspective on the task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS modulates the peak timing in the temporal module while recruiting proximal muscles in the corresponding spatial module.\n\nAuthor summaryThere are at least two fundamental questions in motor neuroscience and related research areas: 1) how does the central nervous system (CNS) control many joints and muscles and 2) how does the CNS generate numerous repertories of movement patterns. One possible answer to question 1) is that the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. One possible answer to question 2) is that the CNS modulates the spatial and/or temporal module depending on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and the task-dependent modulation of those modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the modules and those task-dependent modulations while overcoming the shortcomings inherent to previous methods. We further show that the tensor decomposition provides a new perspective on how the CNS switches between walking and running. The CNS modulated the peak timing in the temporal module while recruiting proximal muscles in the corresponding spatial module.

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bioRxiv2019

3D Exploration of the Brainstem in 50-micron Resolution MRI

The brainstem, a structure of vital importance in the mammals, is currently becoming a principal focus in cognitive, affective and clinical neuroscience. Midbrain, pontine and medullar structures are the epicenter of conduit, cranial nerve and such integrative functions as consciousness, emotional processing, pain and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Moreover, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community at large for applications in basic neuroscience as well as in neurology, psychiatry and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.

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bioRxiv2019

Interrogating theoretical models of neural computation with deep inference

1A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon - whether behavioral or a pattern of neural activity - and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

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bioRxiv2019

Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

ABSTRACTA central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting-state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting-scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders. AUTHOR SUMMARYOur mental lives are made up of a series of predictions about the world calculated by our brains. The calculations that produce these predictions are a result of how areas in our brain interact. Measures based on graph representations can make it clear what information can be combined and therefore help us better understand the computations the brain is performing. We make use of cutting-edge techniques that overcome a number of previous limitations to identify specific shapes in the functional brain network. These shapes are similar to hierarchical processing streams which play a fundamental role in cognitive neuroscience. The importance of these structures and the technique is highlighted by how they change under different task constraints and in individuals diagnosed with psychiatric disorders.

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bioRxiv2025

Retroreflective beam multiplexing enables high-throughput two-photon voltage imaging in vivo

Voltage imaging demands technical innovations to realize its full potential in neuroscience. Although two-photon microscopy is widely used in brain research, its imaging speed is insufficient for high-throughput voltage recordings. To overcome this limitation, we developed an all-optical beam multiplexing engine via retroreflection from two near-parallel mirrors. This approach integrates seamlessly with standard two-photon microscopes, enabling kilohertz-frame-rate imaging across a 16-fold expanded field of view while maintaining synaptic resolution. Operating at over 220 megapixels per second, it enables submillisecond imaging of dendritic propagation of action potentials and simultaneous voltage recording from more than 200 hippocampal neurons in head-fixed, behaving mice. This advancement unlocks new possibilities for large-scale, high-speed functional studies in neuroscience.

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bioRxiv2025

Towards Video-LLM Driven Workflow for Behavioral Segmentation and Scoring in Mice Performing a Skilled Water Reaching Task: An Evaluation of Recent LLM Models

SignificanceBehavior scoring is labor-intensive and subjective, introducing variability in results. Large Language Models (LLMs) capable of video understanding offer a transformative solution to manual scoring, crucial for accelerating and standardizing neuroscience workflows. AimWe sought to benchmark state-of-the-art video LLMs (Gemini 2.5 Pro, Qwen3-VL, and VideoLLaMA3) for automated behavioural segmentation and scoring of mice performing a water-reaching task. ApproachVideos of mice performing water reaching from the front view were analysed by the LLMs. Accuracy was compared across different models and against prompt adjustments within Gemini. To assess classification determinants, video fidelity was altered through pixel interpolation and key regions blurred (paws/snout-mouth). In addition, the models were asked to describe the mouses actions over time. ResultsGemini 2.5 Pro (0.74 {+/-} 0.12 accuracy) and Qwen3-VL-30B (0.67 {+/-} 0.13) exhibited ability to classify trial outcomes. Reliable classification required a minimum pixel resolution of 0.28 mm per pixel. Accuracy is significantly reduced upon obscuring the snout-mouth area. In 549/1058 of videos, Gemini 2.5 Pro also provided completely accurate frame-to-frame behaviour segmentations. ConclusionsVideo-LLMs offer potential to accelerate neuroscience by providing scalable, objective quantification of goal-directed behaviors. By producing temporal annotations, Gemini enables fast first-pass labelling that markedly streamlines manual dataset curation.

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bioRxiv2026

Systematic fMRI signal differences across cohorts alter lifespan connectome trajectories

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCPs widespread usage, even modest protocol differences between cohorts-and their downstream effects-can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.

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bioRxiv2026

Balancing Stability and Flow in Hippocampal Networks via Inductive Bias and Learned Symmetry Breaking

The hippocampus must balance stable memory representations with internally generated sequential dynamics underlying replay and prediction. How hippocampal circuitry achieves both remains unclear. Here, we show that recurrent neural networks trained on prediction tasks converge to a mixed-symmetry dynamical regime, in which dominant symmetric recurrence stabilizes an attractor while a weaker antisymmetric component induces directed flow. This structure accelerates learning and supports robust replay and prediction. We further show that such symmetry breaking can arise from biologically plausible spike-timing-dependent plasticity (STDP) rules, yielding a tilted Mexican-hat connectivity profile without explicit architectural constraints. Initializing networks with CA3-like structured connectivity biases learning toward this regime, and improves optimization efficiency and performance. These results suggest that hippocampal computation reflects an interaction between biologically constrained circuit structure and task-driven learning, with partial symmetry breaking providing a low-dimensional control principle for balancing stability and flow in sequence generation. Subject Areascomputational neuroscience, systems neuroscience, neural dynamics, learning, memory

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