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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 541 records · Page 30Linked to original sources

bioRxiv2026

A Whole-Brain Dynamical Framework Linking Resting-State Activity to TMS-Evoked Responses

A major challenge in systems neuroscience is understanding how external perturbations interact with ongoing brain activity. Transcranial magnetic stimulation (TMS), increasingly used in both basic and clinical neuroscience and often combined with electroencephalography (EEG), provides a unique opportunity to probe this interaction. However, how intrinsic dynamics constrain the propagation of TMS-evoked activity remains poorly understood. In particular, effective connectivity (EC)--capturing directed, state-dependent interactions between brain regions--is thought to critically shape perturbational spread, yet remains difficult to estimate at the whole-brain EEG level. Here we introduce an analytically tractable, generative whole-brain model that links spontaneous EEG activity to cortical responses under perturbation. By deriving a closed-form expression for the models cross-spectral density, we directly fit empirical resting-state EEG spectra and infer biophysically interpretable local dynamical parameters without time-domain simulations. We then estimate stimulation-site-specific EC using only a small fraction of the TMS-EEG trials. The resulting model accurately predicts the spatiotemporal structure of TMS-evoked potentials (TEPs) in unseen trials. Moreover, even without subject-specific refitting, group-level EC templates capture canonical site-specific propagation motifs underlying single-subject early TMS responses. Together, our results establish an analytical framework for individualized whole-brain modeling of TMS-EEG with potential applicability to model-based neuromodulation.

neuroscience↗

bioRxiv2026

Neural-vocal phase coupling reveals structured timing in birdsong production

Understanding how neural population activity is temporally coordinated with behavior remains a central challenge in neuroscience. Songbirds provide a powerful model system for addressing this question because learned vocal production requires precise coordination among neural dynamics, temporally structured motor output, and auditory feedback. However, quantifying neural-vocal interactions is challenging because both neural and acoustic signals are rhythmic, noisy, and highly nonstationary. Here, we investigate neural-vocal coordination during spontaneous canary singing using simultaneous recordings of neural population activity in a forebrain region of the song system and vocal behavior. Using a phase-resolved cross-correlation framework combined with surrogate-based statistical validation, we quantify neural-vocal interactions in short and highly variable song segments. Our analysis reveals that neural-vocal interactions are organized into distinct temporal regimes comprising positive, near-zero, and negative lags, consistent with neural activity preceding, accompanying, or following vocal output. The coexistence of these regimes is consistent with the integrative role of the recorded region, which receives auditory input, contributes to premotor control, and participates in the neural circuitry supporting song learning and the ongoing maintenance of adult song. We further find that correlated and anticorrelated interactions coexist throughout singing, with anticorrelated interactions consistently concentrated around near-zero lags. These anticorrelations identify periods in which decreases in neural population activity are closely aligned with sound production, revealing biologically relevant information that is obscured by analyses performed over complete song renditions. Together, these results uncover a robust temporal structure linking neural population activity to vocal behavior and provide a broadly applicable framework for extracting transient neural-behavioral interactions from complex biological signals. Author summaryModern neuroscience can simultaneously record the activity of large neural populations, yet extracting meaningful relationships between neural activity and behavior remains challenging because natural behaviors are highly variable. Songbirds provide a unique opportunity to study this problem because their learned vocalizations share key features with human speech while remaining experimentally accessible. Here, we analyzed simultaneous recordings of neural population activity and vocal behavior in freely singing canaries. Instead of averaging neural activity across entire songs, we examined brief time windows and combined local correlation analysis with statistical tests to identify reliable interactions between the brain and behavior. We found that these interactions switch among several preferred timing patterns: neural activity can precede vocal output, occur at nearly the same time, or follow acoustic events. This diversity is consistent with the combined roles of the recorded brain region. Our work provides an intuitive and broadly applicable framework for uncovering transient neural-behavioral interactions that remain hidden by traditional time-averaged approaches. Because it requires only simultaneous recordings of two time series and makes minimal assumptions about their dynamics, the framework can be applied to many biological systems involving complex temporal signals.

neuroscience↗

bioRxiv2026

Noise Optimization of Basic Signal Component Extraction for Cryogenic and On-Scalp Magnetoencephalography (MEG)

Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal magnetic fields with higher sensitivity to more complex, higher order spatial components, leading to improved source localization. The accuracy of MEG and OPM-MEG source localization relies on data preprocessing techniques to isolate the neuronal fields from other magnetic and biomagnetic sources through signal space separation, rejection, and suppression methods. Current preprocessing methods risk rejecting brain signals of interest, or can spread sensor noise artifacts unknowingly. Here we propose a novel preprocessing method for MEG, and test the extent to which it overcomes limitations of prior methods. Specifically, we derive, apply, and assess a novel signal space separation (SSS) method with Fosters inverse, a weighted matrix inversion protocol that can utilize information about the MEG sensor noise and artifacts to reconstruct neuronal activity. With simulations, phantom head cryogenic MEG recordings, and subject recordings with two OPM-MEG systems, we show that Fosters inverse with SSS offers a more robust and stable reconstruction of the neuronal magnetic fields, especially in the face of sensor noise and artifacts. As the field of cognitive neuroscience continues to embrace MEG and OPM-MEG, Fosters inverse with SSS offers a robust and powerful data preprocessing technique for reducing noise and improving source localization of the underlying neuronal currents.

neuroscience↗

bioRxiv2026

A stereotaxic atlas of primary cortical areas in the developing rat brain from postnatal days 8 to 20

Postnatal brain growth is non-linear, making precise stereotaxic targeting in the developing rat neocortex difficult without age-specific knowledge of cortical area locations. Traditional atlases visualize brain slices in the coronal plane, which can obscure top-down areal boundaries and sub-domains. To address these shortcomings, we created a developmental stereotaxic atlas that maps the neocortex of postnatal day (P) 8, P12, P16, and P20 in Sprague-Dawley and Long-Evans rats onto a coordinate grid. After using a stereotaxic device to create a grid of fluorescent probes, we extracted the brain, flattened neocortical tissue, and stained it for cytochrome oxidase, which is predominantly found in layer IV of primary cortical areas. Reconstructions of the primary somatosensory, auditory, and visual cortices demonstrate high structural reproducibility within each age and strain group. Our atlas captures the location of primary cortical areas at these 4 ages, showing that neocortical expansion is non-isometric, expanding preferentially along the rostral-caudal axis. Finally, we complement these top-down maps by extracting local neocortical surface angles from an existing coronal atlas, enabling proper electrode orientation to be tangential to the developing neocortex. Ultimately, this anatomically verified resource provides a standardized blueprint that eliminates resource-intensive trial-and-error mapping and maximizes experimental reproducibility in developmental systems neuroscience. Significance StatementTargeting specific neocortical areas in developing rats is uniquely challenging because non-linear brain expansion renders scaled adult coordinates inaccurate, while traditional coronal sections obscure top-down areal boundaries. To resolve this, we established a top-down stereotaxic atlas that maps primary sensory cortices onto flattened, cytochrome oxidase-stained tissue across early postnatal development (P8-P20) in both Sprague-Dawley and Long-Evans rats. By combining equidistant coordinate grids with empirical cortical surface angles, this resource provides an accurate, reproducible surgical blueprint. This reference tool eliminates trial-and-error coordinate mapping, reduces animal waste, and maximizes experimental precision for the developmental neuroscience community.

neuroscience↗

bioRxiv2026

Multimodal Evidence for the Effectiveness of a Digital Behaviour Change Platform: Behavioural, Biomarker, and EEG Outcomes in University Students

Digital behaviour change interventions have emerged as a promising approach for improving health, well-being, and performance, yet relatively few studies have evaluated their effects using objective biological and neurophysiological measures. The present study examined the effectiveness of Autonomic, a neuroscience-informed digital coaching platform designed to improve student well-being through personalized behavioural interventions targeting sleep, stress, mood, energy, and focus. Thirty university students engaged with the platform for ten weeks and completed behavioural assessments, biomarker collection, and electroencephalographic (EEG) testing at baseline, five weeks, and ten weeks. Behavioural outcomes included self-reported ratings of focus, energy, sleep, mood, and stress. Biological measures included salivary cortisol and tear fluid dopamine concentrations. EEG assessments included resting-state recordings, frontal theta activity during a working memory task, and N200/P300 event-related potentials during a visual oddball task. Significant improvements were observed in self-reported focus, energy, and sleep quality across the intervention. Electrophysiological measures demonstrated reduced frontal theta power during working memory and shorter P300 latencies during attentional processing, consistent with more efficient cognitive processing following the intervention. Although cortisol, dopamine, and resting-state EEG measures did not reach statistical significance, all exhibited changes in the predicted direction. Collectively, these findings provide converging behavioural, biological, and neurophysiological evidence supporting the effectiveness of Autonomic. More broadly, the study demonstrates the value of combining objective biomarkers and EEG with traditional behavioural assessments when evaluating digital behaviour change interventions and highlights the potential of neuroscience-informed coaching platforms to improve health, well-being, and cognitive functioning in university students.

neuroscience↗

bioRxiv2026

Effects of transcranial focused ultrasound stimulation to human lateral geniculate nucleus on visual perception and steady-state visual evoked potentials

Transcranial ultrasound stimulation (TUS) is an emerging tool to non-invasively modulate neural activity in deep brain areas. A key need in accelerating TUS into cognitive neuroscience and neuropsychiatry is to better understand how different sonication parameters relate to neuromodulatory effects. Here we assess the role of pulse repetition frequency (PRF), a key TUS parameter thought to determine the relative contribution of molecular displacement and acoustic radiation force effects on neural tissue using the human subcortical visual pathway as a testbed. We combined frequency-tagged steady-state visual evoked potential (SSVEP) measures of contrast-response with contrast increment detection psychophysics as neural and behavioral readouts of visual pathway function. We used structural MRIs and acoustic simulations to target the lateral geniculate nucleus (LGN). Concurrent with visual stimulus presentation, the left LGN or a more superficial control site were stimulated with a neuronavigated depth-steerable 4-element TUS transducer at a range of PRFs with 68 W/cm2 free-water ISPPA, and a 10% duty cycle. An effective white-noise auditory mask blinded participants to stimulation conditions. Recordings from 25 neurotypical participants failed to detect any impact of TUS on SSVEP response amplitude, SSVEP response latency, or perceptual behavior. Analysis of simulations generated from the measured transducer positions grant reasonably high confidence that the LGN was within the TUS focus in most participants, with no correlation between targeting accuracy and changes in activity during TUS. Our results provide a cautionary note about the effect size of neuronavigated TUS for online causal manipulations in cognitive and clinical neuroscience.

neuroscience↗

bioRxiv2026

A Low-Cost, Modular Hardware and Software Platform for Head-Fixed Mouse Decision-Making Tasks

Head-fixed behavior in rodents is a foundational technique in systems neuroscience which enables use of sophisticated imaging techniques in combination with animal behavior. However, accessibility of head-fixed behavior techniques is limited. Animal training consumes a large amount of experimenter labor and commercial setups, when available, are largely inflexible and financially burdensome. Here, we present a low-cost, modular, and open-source hardware and software implementation for head-fixed rodent decision-making tasks. Our design lowers experimenter labor and enables large teams of researchers to participate in animal training with minimal experimenter error using a simple touchscreen GUI and automated training progression. We demonstrate the efficacy of the platform by training a cohort of animals in a two-choice probabilistic rapid-reversal task in which mice continuously update action choices based on recent reward history. The presented design lowers the barrier to entry for laboratories seeking to conduct head-fixed rodent behavior and provides modular solutions for developing custom rigs based on experimental demands. Significance StatementHead-fixation in behaving rodents is a common neuroscience procedure that enables the use of electrical or optical techniques for neural circuit interrogation. While commercial head-fixed behavior platforms are available, their high cost and inflexibility make them unsuitable for modification or high-throughput training. This study presents a novel open-source hardware and software system for mouse head-fixed behavior. The modular design and use of low-cost components enable broad adaptability across behavioral tasks, compatibility with practical research demands, and significantly lowers the barrier to entry to head-fixed behavioral research.

neuroscience↗

bioRxiv2026

CynoBrain: A unified high-resolution framework for cross-modal data integration for macaque brain mapping

The cynomolgus macaque is a widely used primate model in neuroscience, yet existing brain atlas resources remain limited in resolution and lack a standardized framework for integrating structural, molecular, and connectivity data within a common reference space. Here we present CynoBrain, a high-resolution multi-modal brain atlas constructed from population-averaged MRI data at 9.4 Tesla, yielding a 75 micron isotropic template with significant improvement in resolution. A co-registered whole-brain autofluorescence template resolves cytoarchitectural boundaries not visible in MRI template. Three major parcellation schemes, D99 143-area, M132 105-area, and SARM are incorporated in CynoBrain space. Spatial transcriptomic based laminar definition, reconstructed from 2D sections into volumetric common space, provides a six-layer cortical segmentation spanning the whole cortex. As a demonstration of the integrative capability of this framework, 2D retrograde tracing data is reconstructed and mapped to the Cynobrain space, enabling volumetric compilation of 2D macaque brain mapping data across subjects. An online interactive platform was constructed to allow full access to Cynobrain, establishing it as a scalable, open platform for cross-modal data integration for primate neuroscience. Overall, CynoBrain is a unified framework for cross-modal data integration for macaque brain mapping with high-resolution, multiple atlases and transcriptomic based laminar definition.

neuroscience↗

bioRxiv2026

INS-fMRI: a novel method for mapping mesoscale connectome networks in vivo in nonhuman primates

Mapping brain connections is paramount in neuroscience. Lacking has been a method that can systematically map mesoscale (<1mm) connections in the brain in vivo at brainwide scale. Mesoscale mapping is particularly important for NHPs in which information is represented in submillimeter clusters (e.g. cortical columns, striosomes, thalamic rods). Here, we present a method called INS-fMRI, which comprises optical stimulation of single sites in the brain using pulsed Infrared Neural Stimulation (INS), coupled with mapping of functionally connected sites in the brain using ultrahigh field fMRI. It is the only method available for mapping mesoscale connections rapidly (multiple networks mapped within single MRI sessions), systematically (allowing comparison of networks within single individuals), in vivo (no animal sacrifice needed), at whole brain scale, and without viral transfection. This method has broad applicability to a wide range of neuroscience questions. It can be used at any brain site, on different animal models, and on different MRI platforms. The ability to probe circuits repeatedly within single individuals also opens doors for studying changes in mesoscale circuits over time (e.g. for development, aging, disease studies). In addition, as this method circumvents the need for viral transfection, there is exciting potential for human clinical application.

neuroscience↗

bioRxiv2026

Ultrastructural comparison of fixation and cryopreservation methods for brain preservation

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

neuroscience↗

bioRxiv2026

Streamlined Production of Recombinant Adeno-Associated Viruses Using Gateway Cloning Technology for Neural Circuit All-Optical Interrogation

The advent of optogenetics and the rapid development of genetically encoded actuators and reporters for calcium, voltage, neurotransmitters, and other molecules have revolutionized neuroscience research by enabling precise, non-invasive optical interrogation of neural circuits. Successful implementation, however, critically depends on efficient reporter expression in defined neuronal populations. We present a Gateway(R)-based cloning platform providing a robust pipeline for rapid, efficient construction of recombinant adeno-associated virus (rAAV) vectors optimized for neuroscience applications. This platform addresses promoter selection, indicator engineering, and capsid-type optimization, and enables systematic optimization of vector components across a range of optical tools. The benefit of this modular approach is illustrated in the case of ULoVE, a two-photon microscopy method based on acousto-optic deflectors (AODs). This method provides serial light-targeting with kHz sampling rates and high signal-to-noise ratio in vivo, which imposes stringent requirements on indicator expression -- including cell-type specificity, sparse labelling, and precise control of expression levels-- efficiently met through the combinatorial flexibility of the Gateway pipeline. As specific examples, Gateway-constructed Cre-driver viruses combined with Cre-dependent reporters enabled cell-type-specific labelling for two complementary applications. In cerebellar Purkinje cells, an L7::Cre driver virus paired with a Gateway-constructed voltage indicator (JEDI2P-Kv) and ULoVE two-photon excitation at ~5 kHz enables resolution of sub-millisecond dendritic voltage dynamics in awake, behaving mice, including optical detection of dendritic spikelets previously accessible only via intracellular electrophysiology. The same driver virus paired with calcium indicators (GCaMP6f, jRGECO1a) resolves climbing fiber-evoked calcium kinetics under the same conditions. In acute cerebellar slices, a kit::Cre driver virus enabled cell-type-specific expression of the optogenetic actuator ChR2(H134R) in molecular layer interneurons, where ULoVE doughnut-pattern photostimulation achieved single-cell, sub-millisecond optogenetic activation with micron-scale spatial resolution. Beyond ULoVE applications, the same sparse, strong labelling also proved suitable for anatomical tracing of axonal projections in cleared cerebellar tissue, resolving individual DCN axon terminals at the granule cell layer --molecular layer boundary. Taken together, our dual-front approach --combining a modular AAV expression pipeline with AOD-based optical acquisition --provides an integrated platform for developing sophisticated optical tools, including but not limited to AOD-based microscopy, for neural circuit interrogation.

neuroscience↗

bioRxiv2026

Toward A Whole-Brain Interface Enabled by High-Resolution Surface Electrophysiology

The goal of building a whole-brain interface - capable of generating a real-time digital representation of the state of the entire central nervous system - has long been framed as a hardware challenge of recording simultaneously from ever-larger numbers of microelectrodes. Advances in high-resolution neural interfaces now enable electrophysiologic interaction with the human nervous system at spatial and temporal resolutions previously unattainable without disrupting tissue architecture or physiology. These capabilities create new opportunities for mechanistic neuroscience, neuroprosthetics, and precision-guided neurosurgery. With the advent of modern microelectrode array technology, however, the whole-brain interface has also become a data challenge. Rather than pursuing large-scale human brain recording purely through a single device placed in a single patient, we demonstrate here that systematic multi-subject aggregation, across brain regions and behavioral paradigms, in a common anatomical framework, can achieve something functionally similar to a whole-brain interface. Here we present an approach to a functional atlas of the human central nervous system derived from high-resolution surface electrophysiology recordings obtained in the first 70 patients implanted with an anatomically conformal, thin-film neural interface positioned in more than 100 locations. The atlas comprises >100,000 electrode deployments and catalogs placement across a broad range of neuroanatomic targets spanning the motor, sensory, language, auditory, and associative domains, as well as brainstem and spinal cord structures that have historically been difficult to access at high electrophysiologic resolution. Across participants, aggregated recordings reveal somatotopic organization, including digit representations that follow a lateral-to-medial ordering consistent with classical homuncular maps, with transitions between adjacent representations occurring over spatial scales of approximately 0.8 - 2.0 mm; canonical frequency-band signatures, such as movement-related beta-band suppression and focal high-gamma (70 - 200 Hz) increases during motor and speech behaviors; and task-modulated population dynamics in both evoked and spontaneous activity across cortical, brainstem, and spinal cord regions. These findings yield insight into patterns of mesoscale electrophysiological dynamics conserved across the entire human central nervous system, suggesting that fine-scale functional organization is preserved across individuals when aligned to common anatomical landmarks. We further demonstrate several applications of these high-resolution recordings, including real-time neural decoding from upper extremity motor cortex to enable real-time neuroprosthetic control, and from ventrolateral cortex to decode cortical correlates of spoken language, facial movements, and complex facial expressions of emotion, supporting initial feasibility of combined verbal and nonverbal communication interfaces. Together, these results establish a paradigm for approaching large-scale interfaces with the human brain, as well as a starting point for a scalable, extensible atlas of human electrophysiologic function designed to accelerate discovery across basic neuroscience, translational research, and clinical neurotechnology.

neuroscience↗

bioRxiv2026

High-throughput serial two-photon tomography with BakingTray and BrainSaw

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

neuroscience↗

bioRxiv2026

Glutamatergic Neurons in caudal Pontine Reticular Nucleus (PRNc) Mediate Backward Locomotion in mice

Locomotion is essential for animals to adaptively navigate their environment. However, despite extensive investigation into neural mechanisms underlying forward movement, the brain circuits controlling backward locomotion, a critical motor behavior for navigating confined spaces and avoiding threats, remain poorly understood. Here, using a specially designed behavioral paradigm for backward locomotion in mice, we find that glutamatergic neurons in the caudal pontine reticular nucleus (PRNc) are selectively activated during backward, but not forward, locomotion. Activity increase of these neurons is temporally aligned with backward movement, with its amplitude correlated with speed. Optogenetic manipulations further demonstrate that these neurons are both sufficient and necessary for initiating and sustaining backward walking. Moreover, anatomical tracing reveals that the PRNc broadly integrates bilateral cortical and subcortical inputs and projects extensively to brain-wide and spinal cord networks. These findings suggest PRNc as a previously unrecognized brainstem center in direction-specific locomotor control circuits. Key findings of this work and our initial identification of this brainstem locomotor node were presented at the Society for Neuroscience Annual Meeting in November, 15th, 2025 (SfN Neuroscience 2025, San Diego, CA; Program No. PSTR027.02).

neuroscience↗

bioRxiv2026

Neuroline: an integrated modular pipeline for FAIR multimodal neurophysiology data processing

The adoption of FAIR principles for code and data sharing in systems neuroscience is hindered by the size and diversity of datasets, the multitude of data formats and the complexity of analysis workflows. These challenges have led to the development of various data standards and modality-specific processing and analysis tools. However, it remains difficult to integrate the diverse processed data streams originating from state-of-the-art neurophysiology experiments into a common format, which is crucial for cross-modal analyses and open, FAIR sharing of data. To address this, we developed Neuroline, a Python package for processing neurophysiology data (electrophysiology, optical imaging, and behaviour) that combines multimodal integration, modularity, and standardised output within a single pipeline. It leverages established packages and consolidates the resulting processed data into the standardised Neurodata Without Borders (NWB) format. Neuroline provides a unified interface while preserving flexibility and extensibility. Here we describe the implementation and challenges of a pipeline capable of integrating different types of neuroscientific data and packages. Finally, we address how Neuroline contributes to the sustainability and accessibility of neuroscience data and software.

neuroscience↗

bioRxiv2026

Physical sensorimotor contingencies facilitate learning in monkeys

Animal studies in neuroscience routinely use virtual tasks that deviate significantly from the animals' natural behavioral repertoire. This choice is often necessary for conducting careful and controlled experimentation. However, this approach raises a fundamental question: do virtual tasks expose neural computations and mechanisms associated with naturalistic tasks? In this study, we explore how naturalistic settings influence learning timescale and generalization behavior in rhesus monkeys. By training animals in a maze-solving task and comparing their behavior across two environments, we observe significant differences. Specifically, we found that a realistic setup using physical objects enabled learning that was orders of magnitude faster than the learning timescale observed in a comparable computerized virtual environment. Furthermore, we noted limited transfer of performance between the physical and virtual settings. These findings underscore the need for understanding how task environments shape learning and generalization, with potential implications for the design of future neuroscience and AI experiments.

neuroscience↗

bioRxiv2026

A Kernel-Spectral Index for Single-Neuron Input-Output Complexity

The computational capacity of individual neurons in the brain is a central problem in computational neuroscience and an increasingly relevant link between neuroscience and AI. Recent work fitting artificial neural networks to detailed cortical pyramidal neurons showed that reproducing their millisecond-precision input-output behavior can require temporal convolutional networks with five to eight layers, suggesting that cortical neurons may function as deep computational units inside brain circuits. Yet current quantitative studies of single-neuron complexity are largely surrogate-based; consequently, the obtained results are difficult to interpret, and the process often demands massive computational resources. We introduce the Spectral Complexity Index (SCI), a white-box measure that represents presynaptic histories with an explicit kernel geometry and quantifies how many ordered spectral modes are needed to explain somatic response energy. Across 35 biophysically detailed human and mouse cortical neuron models, SCI yields a statistically significant separation of the two species, strongly agrees with the DNN-based Functional Complexity Index on these cells (Spearman correlation: {rho}=0.797), and remains stable under sampling and kernel perturbations. Additionally, compared to existing methods such as FCI, the computational cost of SCI is reduced to approximately 1/10 or less. SCI turns single-neuron complexity from a black-box surrogate property into an interpretable spectral object, enabling reproducible, fast, and efficient comparisons of neuronal computation across cells and species.

neuroscience↗

bioRxiv2016

Expanding perspectives on cognition in humans, animals, and machines

Over the past decade neuroscience has been attacking the problem of cognition with increasing vigor. Yet, what exactly is cognition, beyond a general signifier of anything seemingly complex the brain does? Here, we briefly review attempts to define, describe, explain, build, enhance and experience cognition. We highlight perspectives including psychology, molecular biology, computation, dynamical systems, machine learning, behavior and phenomenology. This survey of the landscape reveals not a clear target for explanation but a pluralistic and evolving scene with diverse opportunities for grounding future research. We argue that rather than getting to the bottom of it, over the next century, by deconstructing and redefining cognition, neuroscience will and should expand rather than merely reduce our concept of the mind.

Animal Behavior and Cognition↗