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Neuroscience

Find bioRxiv neuroscience preprints. Search neural circuits, brain imaging and behavior in indexed metadata; check the original manuscript and its version. Preprints are not peer reviewed.

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bioRxiv2019

Data architecture and visualization for a large-scale neuroscience collaboration

Effective data management is a major challenge for neuroscience labs, and even greater for collaborative projects. In the International Brain laboratory (IBL), ten experimental labs spanning 7 geographically distributed sites measure neural activity across the brains of mice making perceptual decisions. Here, we report a novel, modular architecture that allows users to contribute, access, and analyze data across this collaboration. Users contribute data using a web-based electronic lab notebook (Alyx), which automatically registers recorded data files and uploads them to a central server. Users access data with a lightweight interface, the Open Neurophysiology Environment (ONE), which searches data from all labs and loads it into MATLAB or Python. To analyze data, we have developed pipelines based on DataJoint, which automatically populate a website displaying a graphical summary of results to date. This architecture provides a new framework to contribute, access and analyze data, surmounting many challenges currently faced by neuroscientists.

neuroscience↗

bioRxiv2019

Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks

Conventional deep neural networks capture essential information processing stages in perception. Deep neural networks often require very large volume of training examples, whereas children can learn concepts such as hand-written digits with few examples. The goal of this project is to develop a deep spiking neural network that can learn from few training trials. Using known neuronal mechanisms, a spiking neural network model is developed and trained to recognize hand-written digits with presenting one to four training examples for each digit taken from the MNIST database. The model detects and learns geometric features of the images from MNIST database. In this work, a novel biological back-propagation based learning rule is developed and used to a train the network to detect basic features of different digits. For this purpose, randomly initialized synaptic weights between the layers are being updated. By using a neuroscience inspired mechanism named synaptic pruning and a predefined threshold, some of the synapses through the training are deleted. Hence, information channels are constructed that are highly specific for each digit as matrix of synaptic connections between two layers of spiking neural networks. These connection matrixes named information channels are used in the test phase to assign a digit class to each test image. As similar to humans abilities to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to conventional deep learning methods checked on MNIST dataset.

neuroscience↗

bioRxiv2019

Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience.

Breakthrough technologies for monitoring and manipulating single-neuron activity provide unprecedented opportunities for whole-brain neuroscience in larval zebrafish1-9. Understanding the neural mechanisms of visually guided behavior also requires precise stimulus control, but little prior research has accounted for physical distortions that result from refraction and reflection at an air-water interface that usually separates the projected stimulus from the fish10-12. Here we provide a computational tool that transforms between projected and received stimuli in order to detect and control these distortions. The tool considers the most commonly encountered interface geometry, and we show that this and other common configurations produce stereotyped distortions. By correcting these distortions, we reduced discrepancies in the literature concerning stimuli that evoke escape behavior13,14, and we expect this tool will help reconcile other confusing aspects of the literature. This tool also aids experimental design, and we illustrate the dangers that uncorrected stimuli pose to receptive field mapping experiments.

neuroscience↗

bioRxiv2025

The Overlap Area as a Novel Measure of Effect Size in Neuroscience Research

In experimental biomedical research, a common concern is whether a manipulation produces a biologically meaningful effect. Another concern is whether effect sizes are reliable, statistically significant, and generalizable. Traditional effect size measures, such as Cohens d, quantify mean differences but ignore variance heterogeneity between groups. This can result in biased effect size estimates and a lack of thresholds for statistical significance testing. Motivated by this, we introduce a novel effect size measure, termed overlap area (OA), which quantifies the difference between the population distributions of the experimental and control groups. A robust Bayesian method estimated OA, and random resampling determined OA thresholds for statistical significance in single and replicated experiments. Simulations confirmed the approachs sensitivity and robustness. Applied to a real-world dataset, OA revealed that environmental enrichment affects the mouse brain synaptome. Moreover, we developed an open-source toolbox supporting OA as a powerful new measurement for conducting robust, reliable, and reproducible analyses of manipulation effects in neuroscience and related fields.

neuroscience↗

bioRxiv2026

Preparation and application of YFV-17D-derived Anterograde Trans-neuronal Viral Vectors for Neuroscience Research

Trans-neuronal viruses that spread between synaptically connected neurons have become invaluable tools in neuroscience, enabling circuit mapping and targeted delivery of genetic material to cells within defined pathways. Although most available trans-neuronal viral tracers propagate retrogradely, an anterograde trans-neuronal virus that spread from presynaptic to postsynaptic neurons would substantially expand experimental capabilities. We previously demonstrated that YFV-17D--a live attenuated yellow fever vaccine used clinically for decades--spreads anterogradely along neuronal circuits. We further developed a suite of recombinant YFV-17D-based vectors for diverse applications, including tracing monosynaptic projectome of defined neuronal cell types, multiplex mapping of parallel pathways, and trans-neuronal genetic manipulation with minimal neurotoxicity. We have now tested and optimized procedures for vector production and application. Based on these optimizations, here, we present a two-part guide for preparing and deploying this viral vector system to map brain connectivity. The first part describes the molecular biology and cell culture workflows required to generate high-quality viral vectors, yielding titers of [~]1 x 10^10 to 5 x 10^11 genomic copies per milliliter within approximately two weeks. The second part outlines optimized animal procedures, including intracranial injections, perioperative care, and experimental considerations tailored to specific aims and vector variants. Depending on the application, efficient trans-neuronal tracing can be achieved within 1-4 weeks following delivery.

neuroscience↗

bioRxiv2026

A canine brain bank for comparative neuroscience and brain aging research

Companion animal brain banking has been recognized as a valuable approach for translational aging and dementia research. However, realizing the full value of canine brain banks depends on optimizing the methods that are used to collect and preserve the tissue. Whole brain perfusion fixation is one promising approach, but it is not yet well described in dogs. Here we describe the development of methods for a canine brain bank (currently n = 55), including whole brain perfusion fixation via aortic cannulation and brain extraction. We assessed perfusion quality using gross examination, post-perfusion CT, and histological clearance of blood vessels. We found that body weight and average flow rate per body weight were each significantly correlated with perfusion quality in our cohort. To illustrate the kind of analysis the bank could facilitate, we next performed a preliminary study of brain aging, one of our primary planned research applications. Using a pixel classifier applied to whole slide images, we quantified lipofuscin burden, and in this preliminary cohort found that it increased strongly with age in both the thalamus and hippocampus. In the hippocampus, lipofuscin burden was also elevated in dogs with owner-reported cognitive dysfunction, although the current cohort is too small to determine to what extent this association is independent of age. Preliminary electron microscopy studies also confirmed that perfusion fixed tissue from the bank is amenable to ultrastructural analysis. This work describes one approach for canine brain perfusion fixation and introduces a brain tissue resource that may help support future neuroscience research.

neuroscience↗

bioRxiv2026

A behaviourally normed database of 1,377 natural sounds for auditory cognition and neuroscience

Natural-sound research requires stimulus sets that combine acoustic standardization with detailed behavioural characterization. We present 1,377 two-second sounds representing 240 expert-defined source--action classes. We call this database "MaMa Sounds", as it resulted from the collaborative effort of two academic teams in Maastricht and Marseille. The sounds were manually curated, segmented, sampled at 16 kHz, and labelled with a noun identifying the source and a verb identifying the action. We release deidentified trial-level identification and familiarity data together with multiple per-sound norms (e.g., identification accuracy, confidence and agreement; familiarity), along with overall norms derived with principal component analysis. Noun, verb, and joint noun--verb norms are provided as direct means and medians with the number of contributing observations. This battery preserves process-specific information, while two principal-component scores provide compact overall behavioural-identifiability measures derived from response ease, semantic correspondence, agreement, and familiarity. The repository also contains deterministic response-cleaning code, participant and reference Word2Vec representations, and code reproducing the public sound-level tables. The resource supports stimulus selection, matching, and continuous modelling in auditory cognition and neuroscience.

neuroscience↗

bioRxiv2014

Big Behavioral Data: Psychology, Ethology and the Foundations of Neuroscience

Behavior is a unifying organismal process through which genes, neural function, anatomy and environment converge and interrelate. Here we review the current state and sketch the future impact of accelerating advances in technology for behavioral studies, focusing on rodents as an exemplar. We frame our analysis in three dimensions: (1) degree of experimental constraint, (2) dimensionality of data, and (3) level of description. We argue that ethomics, fueled by \"big behavioral data\", presents challenges proportionate to its promise and describe how these challenges might be met through opportunities afforded by the two rival conceptual legacies of 20th century behavioral science, ethology and psychology. We conclude that although \"ethomes\" are not the goal, big behavioral data has the potential to transform and unify these two disciplines and to solidify the foundations of others, including neuroscience, particularly if the data is as open as it is copious and quantitative.

Animal Behavior and Cognition↗

bioRxiv2017

Bridging Computational Neuroscience and Machine Learning on Non-Stationary Multi-Armed Bandits

Fast adaptation to changes in the environment requires both natural and artificial agents to be able to dynamically tune an exploration-exploitation trade-off during learning. This trade-off usually determines a fixed proportion of exploitative choices (i.e. choice of the action that subjectively appears as best at a given moment) relative to exploratory choices (i.e. testing other actions that now appear worst but may turn out promising later). The problem of finding an efficient exploration-exploitation trade-off has been well studied both in the Machine Learning and Computational Neuroscience fields. Rather than using a fixed proportion, non-stationary multi-armed bandit methods in the former have proven that principles such as exploring actions that have not been tested for a long time can lead to performance closer to optimal - bounded regret. In parallel, researches in the latter have investigated solutions such as progressively increasing exploitation in response to improvements of performance, transiently increasing exploration in response to drops in average performance, or attributing exploration bonuses specifically to actions associated with high uncertainty in order to gain information when performing these actions. In this work, we first try to bridge some of these different methods from the two research fields by rewriting their decision process with a common formalism. We then show numerical simulations of a hybrid algorithm combining bio-inspired meta-learning, kalman filter and exploration bonuses compared to several state-of-the-art alternatives on a set of non-stationary stochastic multi-armed bandit tasks. While we find that different methods are appropriate in different scenarios, the hybrid algorithm displays a good combination of advantages from different methods and outperforms these methods in the studied scenarios.

animal behavior and cognition↗

bioRxiv2020

Practical Design and Implementation of Animal Movements Tracking System for Neuroscience Trials

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSThe nervous system functions of an animal are predominantly reflected in the behaviour and the movement, therefore the movement-related data and measuring behavior quantitatively are crucial for behavioural analyses. The animal movement is traditionally recorded, and human observers follow the animal behaviours; if they recognize a certain behaviour pattern, they will note it manually, which may suffer from observer fatigue or drift. ObjectiveAutomating behavioural observations with computer-vision algorithms are becoming essential equipment to the brain function characterization in neuroscience trials. In this study, the proposed tracking module is eligible to measure the locomotor behaviour (such as speed, distance, turning) over longer time periods that the operator is unable to precisely evaluate. For this aim, a novel animal cage is designed and implemented to track the animal movement. The frames received from the camera are analyzed by the 2D bior 3.7 Wavelet transform and SURF feature points. ResultsImplemented video tracking device can report the location, duration, speed, frequency and latency of each behavior of an animal. Validation tests were conducted on the auditory stimulation trial and the magnetic stimulation treatment of hemi-Parkinsonian rats. Conclusion/ SignificanceThe proposed toolkit can provide qualitative and quantitative data on animal behaviour in an automated fashion, and precisely summarize an animals movement at an arbitrary time and allows operators to analyse movement patterns without requiring to check full records for every experiment.

animal behavior and cognition↗

bioRxiv2020

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Neuroscience incorporates knowledge from a range of scales, from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB(R) scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model. Information Sharing StatementBoth the source code and documentation of the Subcellular Workflow are available at https://github.com/jpgsantos/Subcellular_Workflow and licensed under GNU General Public License v3.0. The model is stored in the SBtab format (Lubitz et al. 2016). Model reduction, parameter estimation and global sensitivity analysis tools are written in MATLAB(R) (RRID:SCR_001622) and require the SimBiology(R) toolbox. Conversion script to VFGEN (Weckesser 2008), MOD and SBML (RRID:SCR_007422) is written in R (RRID:SCR_001905). Conversion to SBML requires the use of libSBML (RRID:SCR_014134). Validations are run in COPASI (RRID:SCR_014260; Hoops et al. 2006), NEURON (RRID:SCR_005393; Hines and Carnevale 1997) and with the subcellular simulation setup application (RRID:SCR_018790; available at https://subcellular.humanbrainproject.eu/model/simulations) that uses a spatial solver provided by STEPS (RRID:SCR_008742; Hepburn et al. 2012) and network-free solver NFsim (available at http://michaelsneddon.net/nfsim/). The medium spiny neuron model (Lindroos et al. 2018) used in NEURON simulations is available in ModelDB database (RRID:SCR_007271) with access code 237653. The FindSim use case model is available in https://github.com/BhallaLab/FindSim (Viswan et al. 2018).

systems biology↗

bioRxiv2022

Twitter as a community communication tool for international neuroscience conferences

Scientific conferences increasingly include online aspects. Some are moving to be entirely virtual while others are adopting hybrid models in which there are both in-person and virtual elements. This development of opportunities for people to attend conferences virtually has the potential to both reduce their environmental impact and to make access to them more equitable. An issue with virtual conference participation that has been raised, however, is that there is a reduction in informal communication between attendees. This is an important deficit as such informal contacts play a significant role in both knowledge transmission and professional network development. One forum where some informal communication around conferences does occur is Twitter, with this being encouraged by some conferences. It is not clear, though, to what extent the informal networks built around this service help reduce the problems of unequal access see with in-person conferences. Focusing on geographical equity, we looked at Twitter usage surrounding four international conferences between 2010 and 2021. It was found that engagement with conference hashtags increased steadily over time, peaking in 2019. Users represented 9% of conference attendees and were primarily located in Europe and North America, communicating primarily in English (97% of tweets). Hub nodes within the interaction network were also primarily located in these regions. East Asia had fewer users than would be expected based on neuroscience publication numbers from that region. What users there were in East Asia were engaged with less than were users in other regions. It was found that the overall interaction network showed a rich-club structure, where users with more connections tend to interact more with others with similar connections. Finally, it was found that users in Europe and North America tend to communicate with other users in their own regions whereas users in the rest of the world direct their interactions out of their region. These results suggest that although conference-related Twitter use has been successful to some degree in opening up access, there are some notable limitations in its usage that may mirror aspects of inequality inherent to in-person conferences. How to build equitable informal communication networks around virtual conferences remains a challenging question that requires further discussion.

scientific communication and education↗

bioRxiv2023

Engineering Regionally-Activated Drugs for Neuroscience

The brain is comprised of multiple regions performing distinct functions. Within each of these regions, there are multiple cell types that can affect brain physiology. Finally, within each cell there are multiple signaling pathways, that, when activated or inhibited, control the cells activity, and consequently the brain function. For these reasons, methods that can control the brain with regional, cell-type, and molecular precision have been widely used in neuroscience. However, so far, achieving sustained control over a brain region with that level of specificity relied either on gene delivery or placement of invasive devices. While gene therapy holds great promise, the risks of genomic integration, vector toxicity, vector-directed immune response, high cost, and gene delivery to the brain pose significant challenges. On the other hand, invasive devices enable site-specific delivery of drugs but can also surgically damage the modulated brain region, carrying risks of infection and hemorrhage. Here, we present a new approach that can provide multi-day, noninvasive, site-specific control over specific cell types in the brain without the need to use invasive devices or gene delivery. To achieve this, we introduce a new paradigm called Regionally Activated Interstitial Drugs, or RAID, which delivers a protein-based catalytic centers, or RAID enzymes, to the brain using focused ultrasound blood-brain barrier opening. This catalytic center is designed to attach to the interstitial space in the brain where it remains for days after initial delivery. While the catalytic center is present in the brain, it can locally process an inert BBB permeable prodrug into an active drug, resulting in localized therapy. Our proof-of concept studies demonstrated that the engineered RAID enzymes can retain activity in the brain parenchyma for several days, allowing for noninvasive site-specific induction of neuronal activity that was sufficiently potent to elicit behavioral effects. Overall, the RAID paradigm enabled noninvasive, tunable, temporally-re-solved, site-specific, non-genetic, neuromodulation over multiple days. The RAID paradigm is versatile and can be applied to any enzyme and prodrug pair to control various aspects of central nervous system physiology.

bioengineering↗

bioRxiv2024

A Retrospective Analysis of Career Outcomes in Neuroscience

What factors are associated with different career outcomes among biomedical PhDs? Much of the research to-date has focused on drivers of interest in (and intention to pursue) various careers, especially during graduate school, but fewer studies have investigated the ultimate career outcomes of participants. Even less is known about what factors matter most for groups historically underrepresented in the US STEM workforce, such as for women, some racial and ethnic groups, and persons with disabilities (National Center for Science and Engineering Statistics (NCSES), 2021a). This study reports a new analysis of data from 781 PhD neuroscientists that were obtained from a retrospective survey (reported in Ullrich et al. (2021)) to investigate the factors that influence the career sector in which neuroscience PhDs are employed, and whether there were group differences according to social identity. We find evidence of academia as a "default path" for incoming PhD students, but interest in different careers changes gradually over time. Those who remained in academia had greater acceptance of the structural aspects of academic careers, such as the promotion and tenure process, and greater faculty support during their postdoctoral training. Conversely, prioritizing monetary compensation and wanting varied work were associated with not being in academia, while a strong interest in research was positively associated with being in non-academic research. Somewhat surprisingly, there were few interactions with gender, and no interactions with underrepresentation status. Our findings also underscore the role of advisors, networking, and personal relationships in securing employment in STEM.

scientific communication and education↗

bioRxiv2018

Impact of Human Behavioral Papers at Journal of Neuroscience

A recent policy change at Journal of Neuroscience (JN) has significantly increased editorial \"desk rejections\", reducing the number of papers that are considered for publication. Survey results from 130 scientists suggested that the new policy may have had a particularly large impact on studies that focused on human behavioral techniques. To quantify the effects of the new policy, we gathered data on all papers ever published in JN (~35,000), as well as all papers that had cited the JN papers (~2.7 million papers). We found that the recent change in editorial policy had disproportionately affected rejection rates of human behavioral papers: since 2015, the number of human behavioral papers as a proportion of all papers published in JN has seen a 30% decline. While there has been a long-term declining trend in the journals impact factor, we found that this declining impact factor was shared by both human behavioral papers as well as other papers in the journal. This suggested that whatever may have been the source of the declining impact factor at JN, this source was affecting the various areas of research equally. That is, it was unlikely that papers in any one field were responsible for the declining impact factor number at JN. Indeed, when impact was measured over the long-term, we found that the average human behavioral paper at JN consistently outperformed other papers, generating a significantly higher number of citations per year at 5 and 10 years post publication.

scientific communication and education↗

bioRxiv2018

A common statistical misunderstanding in Psychology and Neuroscience: Do we need normally distributed independent or dependent variables for linear regression to work?

Our ability to draw conclusions from experiments often relies on the application of inferential statistics, such as those included within the general-linear-modeling framework. The purpose of this commentary is to direct attention a common fallacy that can be observed in paper reviews and professional exchanges in the field of Psychology and Neuroscience pertaining to multiple regression analysis. The fallacy concerns the confusion of the requirements on the residuals of a linear-regression model with requirements on the dependent or independent variables themselves. A simple thought experiment, algebraic demonstration, and simulation study can demonstrate this misconception. It is our hope that consideration of the fallacy may help clarify distinctions between technical statistical issues and conceptual interpretation of research findings.

scientific communication and education↗

bioRxiv2026

Neuroscience-Inspired Deep Learning Brain-Machine Interface Decoder

Brain-machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial-temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation.

bioengineering↗

bioRxiv2026

Patterns of genAI bias in guiding prospective undergraduate students: a study of UK neuroscience programmes

Generative artificial intelligence (genAI) tools are increasingly used by prospective higher education (HE) applicants seeking guidance on university and programme selection. Despite rapidly expanding use, little is known about how genAI systems may introduce or amplify bias in undergraduate admissions decision-making. Here, we systematically examined patterns of bias across three widely used genAI chatbots (ChatGPT, Copilot, Gemini) using neuroscience as a representative UK undergraduate programme. We constructed 216 prompts that varied by applicant characteristics (e.g. gender, study type, academic attainment). Each prompt was submitted to all three chatbots, generating 648 responses and 3240 individual programme recommendations. Output responses underwent text analysis (e.g. n-grams, gender-coded language), and national HE markers of esteem (REF21, TEF23, NSS24) were analysed. Applicant grades and priorities produced the strongest effects on genAI outputs. Higher-grade applicants and those prioritising research received significantly more masculine-coded language, independent of applicant gender. N-gram patterns also diverged: high-grade prompts more frequently elicited terms relating to excellence and research intensity, whereas lower-grade prompts produced greater emphasis on widening access. Recommendations were systematically skewed, with higher grades, private schooling, and research-focused priorities increasing the likelihood of recommending elite institutions and programmes with higher entry requirements. Critically, the gender-coded language of outputs predicted institutional characteristics: masculine-coded responses were associated with recommendations featuring higher entry thresholds and stronger research performance, while feminine-coded responses favoured institutions with higher student satisfaction. These findings reveal clear, systematic biases in how genAI guides prospective HE applicants. Such biases risk reinforcing existing educational and socioeconomic inequalities, underscoring the need for transparency, regulation, and oversight in the use of genAI within HE decision-making. HighlightsO_LIGenAI is widely used by HE applicants despite little study of its biases. C_LIO_LI216 prompts across 3 chatbots generated 3240 programme suggestions. C_LIO_LIGrades and priorities drove major shifts in language and recommendations. C_LIO_LIGender-coded wording mapped onto research strength and entry standards. C_LIO_LIGenAI biases may reinforce inequalities in HE admissions decision-making. C_LI

scientific communication and education↗