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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 Preprints are not peer reviewed.

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bioRxiv2025

PhotoNeuro: A compact photodetector for synchronization of visual stimulus presentation during behavioral experiments in neuroscience.

Presenting visual stimuli in neuroscience experiments often requires the combination of analogue signals that carry information about the visual cue presented on the LCD display. Such signals are often sensed by photodetectors and recorded in analogue to digital converter (ADC) acquisition boards. The use of open-source visual programming languages such as Bonsai is becoming more and more popular. They are often used in combination with other open-source hardware such as Arduino development boards. These microcontroller-based boards can be used to automate behavioural experiments: e.g., actuate valves and motors and acquire analogue signals on their ADC channels. LCDs and other modern display allow fast presentation of arbitrary visual stimuli and are widely used for psychophysics and neuroscience experiments. However, most displays do not provide hardware timestamping options and are intrinsically nonlinear. Solving this limitation often requires a direct recording of the light emitted by the display with a photodiode. Such photodetectors are are often amplified at higher voltages and hard to integrate in most common recording systems that use microcontrollers. The other drawback commonly found by neuroscience researchers in commercial devices is the relatively big footprint that the sensor occupies on the screen which, ideally should be minimised so not to interfere with the stimuli presentation. In this paper we present a small footprint photodetector that can be easily replicated and operates at 5V making it suitable to use with common development boards and the visual programming language Bonsai that is commonly used for experiment creation and control. Additionally, we share a version that includes four photodiodes in small area (400 mm2). Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@7bafcforg.highwire.dtl.DTLVardef@5353ccorg.highwire.dtl.DTLVardef@a29f47org.highwire.dtl.DTLVardef@86f5d2org.highwire.dtl.DTLVardef@74da5f_HPS_FORMAT_FIGEXP M_TBL C_TBL

neuroscience↗

bioRxiv2019

A computational knowledge engine for human neuroscience

Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. The goal for this research has largely been to understand how activity across brain structures relates to mental constructs and computations. However, interpretation of fMRI data has often occurred within knowledge frameworks crafted by experts, which have the potential to reify historical trends and amplify the subjective biases that limit the replicability of findings. 1 In other words, we lack a comprehensive data-driven ontology for structure-function mapping in the human brain, through which we can also test the explanatory value of current dominant conceptual frameworks. Ontologies in other fields are popular tools for automated data synthesis,2, 3 yet relatively few attempts have been made to engineer ontologies in a data-driven manner.4 Here, we employ a computational approach to derive a data-driven ontology for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. The data-driven ontology includes 6 domains, each defined by a circuit of brain structures and its associated mental functions. Several of these domains are omitted from the leading framework in neuroscience, while others uncover novel combinations of mental functions related to common brain circuitry. Crucially, the structure-function links in each domain better replicate across articles in held-out data than those mapped from the dominant frameworks in neuroscience and psychiatry. We further show that the data-driven ontology partitions the literature into modular subfields, for which the domains serve as generalizable archetypes of the structure-function patterns observed in single articles. The approach to computational ontology we present here is the most comprehensive functional characterization of human brain circuits quantifiable with fMRI. Moreover, our methods can be extended to synthesize other scientific literatures, yielding ontologies that are built up from the data of the field.

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bioRxiv2026

Co-Designing Research Priorities in Developmental Neuroscience: A Community-Based Participatory Approach

Children and adolescents, the primary participants in developmental neuroscience research, are rarely consulted or included in the research design process. By neglecting their perspectives and needs, we risk limiting the relevance and effectiveness of translational research that may emerge from this work. Here, we report on a process of co-design with adolescents to inform the generation of a new population neuroimaging research programme. We employed a two-study community-based participatory approach and used mixed methods to identify the research priorities of this community. In the first study, 79 secondary school students from four schools were introduced to fundamental neuroscience concepts during five one-hour workshops. In groups, they then designed research studies, centred around topics they deemed important, and were asked to present their ideas through poster presentations, role playing as neuroscientists. Reflexive thematic analysis was conducted on these posters, extracting ten key themes. In the second study, 376 students from four different schools, ranked these ten themes in order of importance. Mental health and stress consistently emerged as the highest priorities. Further exploratory analysis revealed that the priority for exploring everyday routines and understanding anti-social behaviours varied across schools and age groups, while the importance of mental health varied significantly between genders. This work provides an illustration of how to implement place-based participatory approaches at the early stages of a developmental neuroscience research project to maximise inclusivity and potential downstream translation and impact.

neuroscience↗

bioRxiv2026

Convolutional Neural Networks and Neuroscience: A Tutorial Introduction for The Rest of Us

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

neuroscience↗

bioRxiv2026

VFB-MCP: Natural-Language Access to Drosophila Neuroscience Grounded by an Expert-Curated Ontology-Led Knowledgebase

Biological databases store curated knowledge that researchers traditionally access through web interfaces or APIs. To move beyond casual browsing requires domain-specific knowledge and expertise to frame the queries necessary to explore this data. This generates barriers to both experienced users trying to integrate wide-ranging sources of information and for new users entering scientific fields undergoing paradigm shifts such as connectomics. A potentially powerful method to lower these barriers is to facilitate natural-language access to these databases by exposing them to large language models (LLMs) via the Model Context Protocol (MCP). Here we implement this for Virtual Fly Brain (VFB), an expert-curated and ontology-backed knowledgebase of Drosophila neuroscience, providing the precision needed to make recently integrated connectome and molecular data accessible. Benchmarked on 30 neuroscience tasks against a bare LLM and a web-search-assisted LLM, the VFB-MCP-equipped LLM produces precise, verifiable and appropriately quantified answers on 25/30 tasks vs 14/30 for web and 2/30 for bare. The MCP advantage is largest for tasks where data quantification is required (89% vs 11% web). This work establishes MCP layered over ontology-backed knowledge graphs as an effective method to improve LLM response quality for neuroscience and connectomics data, promoting accessibility and accelerating the reliable analysis of complex integrated datasets.

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bioRxiv2026

Braintree: An agentic framework for in-silico vision neuroscience

Vision neuroscience has many of the ingredients needed to conduct experiments entirely in-silico. We need AI systems that can reason over scientific knowledge, generative methods for creating precisely controlled stimuli, image-computable models that can predict neural responses with increasing accuracy, and tools that can analyze and interpret data. Here we take the first step toward making a vision neuroscience experiment end-to-end computable. We introduce Braintree, an agentic framework that can autonomously run multi-experiment studies directly with in-silico models of human visual cortex. Given a hypothesis, Braintree can reason over the current literature, design an experiment, build a stimulus set from algorithmically manipulated or synthesized stimuli, run the experiment on candidate brain encoding models, analyze the responses and then use the results to design even stronger tests. The entire loop runs autonomously and can accept human input at any stage. We evaluated Braintree on three increasingly open-ended scientific problems. First, Braintree reproduced published findings using their original stimuli and then asked whether the same effects could be replicated with entirely new stimulus sets that it constructed. Second, it designed new experiments to find stimulus dimensions that could drive two brain regions differently, even when those regions otherwise had similar response profiles. Third, it searched for experiments that could tell competing models of the same brain region apart. Experiments discovered using just two models also exposed differences across a much broader set of models, suggesting that Braintree had found more general dimensions along which brain models disagree. Braintree can augment the capabilities of a vision neuroscientist as a verifier for existing hypotheses, a test-bed for new ideas, and/or a tool for generating stimuli that can discriminate between competing hypotheses or models of the brain. Braintree shows how AI agents and brain models can work together to make in-silico vision neuroscience experiment computable, allowing vision scientists to explore many more experiment conditions and identify the most informative ones to test on the human brain.

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bioRxiv2016

Building a Web of Linked Data Resources to Advance Neuroscience Research

The fundamental goal of neuroscience is to understand the nervous system at all levels of description, from molecular components to behavior. The complexity of achieving this goal in neuroscience, and biomedicine in general, poses many technical and sociological challenges. Among these are the need to organize neuroscientific data, information, and knowledge to facilitate new scientific endeavors, provide credibility and visibility of research findings, and increase the efficiency of data reuse. Linked Data is a set of principles based on Web technology that can aid this process as it organizes data as an interconnected network of information. This review examines the history, practical impact, potential, and challenges of applying Linked Data principles to neuroscience.

Scientific Communication and Education↗

bioRxiv2021

Factors that Influence Career Choice Among Different Populations of Neuroscience Trainees

Enhancing the diversity of the scientific workforce is critical to achieving the mission of NIH: "To seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce the burdens of illness and disability." However, specific groups have historically been, and continue to be, underrepresented in the biomedical research workforce, especially academia. Career choice is a multi-factorial process that evolves over time; among all trainees, expressed interest in faculty research careers decreases over time in graduate school, but that trend is amplified in women and members of historically underrepresented racial and ethnic groups (Fuhrmann, Halme, OSullivan, & Lindstaedt, 2011; Gibbs, McGready, Bennett, & Griffin, 2014; C. Golde & Dore, 2004; Roach & Sauermann, 2017; Sauermann & Roach, 2012). Neuroscience as a discipline has characteristics that may exacerbate the overall trends seen in the life sciences, such as a greater growth in the number of awarded neuroscience PhDs than in other life sciences fields (US National Science Foundation, 2016b). This work was designed to investigate how career interest changes over time among recent neuroscience PhD graduates, and whether differences in career interests are associated with social identity (i.e. gender and race/ethnicity), experiences in graduate school and postdoctoral training (e.g. relationship with advisor; feelings of belonging), and personal characteristics (e.g. confidence in ones potential to be an independent researcher). We report results from a survey of 1,479 PhD neuroscientists (including 16% underrepresented (UR) and 54% female scientists). We saw repeated evidence that individual preferences about careers in general, and academic careers specifically, predict current career interest. These statistically significant preferences mostly had medium to low effect size that varied by career type. These findings were mediated by social identity and experiences in graduate school and postdoctoral training. Our findings highlight the important influence of the advisor in shaping a trainees career path, and the ways in which academic culture is perceived as unwelcoming or incongruent with the values or priorities of certain groups. For women, issues of work/life balance and structural issues of academia, and for UR women in particular, lower confidence in their ability to be an independent researcher, affected their interest in academia. Both women and UR men in our study report a lower importance of autonomy in their careers. UR respondents report feeling less like they were a part of the social and intellectual community. However, they have formed beneficial relationships with faculty outside their PhD institutions that, particularly for UR women, are associated with increased interest in academia. Our findings suggest several areas for positive growth, ways to change how we think about the impact of mentorship, and policy and programmatic interventions that extend beyond trying to change or "fix" the individual and instead recognize the systemic structures that influence career choices.

scientific communication and education↗

bioRxiv2023

Internet-connected cortical organoids for project-based stem cell and neuroscience education

The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course, and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training. HIGHLIGHTS- Development of cortical organoids as pedagogical tools for undergraduate education. - Organoids implemented in a tissue culture course through cloud-enabled microscopy. - Multielectrode arrays allow for live organoid manipulation in a mathematics course. - Students self-report increased interest in neuroscience and stem cells topics.

scientific communication and education↗

bioRxiv2017

A systematic review of sample size and power in leading neuroscience journals

Adequate sample size is key to reproducible research findings: low statistical power can increase the probability that a statistically significant result is a false positive. Journals are increasingly adopting methods to tackle issues of reproducibility, such as by introducing reporting checklists. We conducted a systematic review comparing articles submitted to Nature Neuroscience in the 3 months prior to checklists (n=36) that were subsequently published with articles submitted to Nature Neuroscience in the 3 months immediately after checklists (n=45), along with a comparison journal Neuroscience in this same 3-month period (n=123). We found that although the proportion of studies commenting on sample sizes increased after checklists (22% vs 53%), the proportion reporting formal power calculations decreased (14% vs 9%). Using sample size calculations for 80% power and a significance level of 5%, we found little evidence that sample sizes were adequate to achieve this level of statistical power, even for large effect sizes. Our analysis suggests that reporting checklists may not improve the use and reporting of formal power calculations.

scientific communication and education↗

bioRxiv2016

Towards an integration of deep learning and neuroscience

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brains specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

Neuroscience↗

bioRxiv2017

Deep Neural Networks In Computational Neuroscience

The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioural responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term \"neural network\" suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g. visual object and auditory speech recognition) to cognitive tasks (e.g. machine translation), and on to motor control (e.g. playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviours, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the result of four directly manipulable elements: input statistics, network structure, functional objective, and learning algorithm. With full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data, DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.

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bioRxiv2019

Closed-loop Neuroscience of brain rhythms: optimizing real-time quantification of narrow-band signals to expedite feedback delivery

AO_SCPLOWBSTRACTC_SCPLOWClosed-loop Neuroscience is based on the experimental approach where the ongoing brain activity is recorded, processed, and passed back to the brain as sensory feedback or direct stimulation of neural circuits. The artificial closed loops constructed with this approach expand the traditional stimulus-response experimentation. As such, closed-loop Neuroscience provides insights on the function of loops existing in the brain and the ways the flow of neural information could be modified to treat neurological conditions. Neural oscillations, or brain rhythms, are a class of neural activities that have been extensively studied and also utilized in brain rhythm-contingent (BRC) paradigms that incorporate closed loops. In these implementations, instantaneous power and phase of neural oscillations form the signal that is fed back to the brain. Here we addressed the problem of feedback delay in BRC paradigms. In many BRC systems, it is critical to keep the delay short. Long delays could render the intended modification of neural activity impossible because the stimulus is delivered after the targeted neural pattern has already completed. Yet, the processing time needed to extract oscillatory components from the broad-band neural signals can significantly exceed the period of oscillations, which puts a demand for algorithms that could minimize the delay. We used EEG data collected in human subjects to systematically investigate the performance of a range of signal processing methods in the context of minimizing delay in BRC systems. We proposed a family of techniques based on the least-squares filter design - a transparent and simple approach, as it required a single parameter to adjust the accuracy versus latency trade-off. Our algorithm performed on par or better than the state-of the art techniques currently used for the estimation of rhythm envelope and phase in closed-loop EEG paradigms.

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bioRxiv2020

Implementing an algorithm for controlling for female MC phase for clinical neuroscience

BackgroundRecent research suggests that hormones and/or menstrual cycle (MC) phase at time of study may significantly affect clinical neuroscience outcomes of interest. Prior research puts forth sound methods for characterizing MC phase in women not using hormonal contraceptives (HC). However, an estimated 40% of premenopausal women in the United States use some form of HC. We developed an algorithm for characterizing hormone levels and MC phase among women both using and not using HC. We have employed this algorithm among female collegiate athletes post-mild traumatic brain injury (mTBI) as female athletes are understudied in the sports mTBI field, MC phase may have important effects on mTBI-related outcomes and controlling for MC phase has not been employed in this population. MethodsThirty female collegiate athletes were studied. Fifteen incurred a mTBI 3-10 days prior to assessment and sixteen were age, ethnicity, and menstrual cycle (MC) phase matched to the injured athletes. MC matching was conducted with retrospective and prospective self-report MC tracking, self-report of HC use, and serum progesterone testing. Results53% (16/30) of females were on HC and 47% (14/30) were not. Of the non-HC users, seven female pairs were in the follicular and one was in the luteal phase. Among the non-HC users, there was 50% agreement in MC phase identification between self-report and progesterone measures and a {kappa} = 0.138. Of the HC users, eight were in the inactive and six were in the active phase of their medication. Among the HC users, average progesterone levels indicated medication compliance (.58ng/mL). ConclusionsThis study provides important methodology and proof-of-concept that MC phase can be used as a control variable for time-sensitive prospective clinical neuroscience studies including those involving brain injury. When studying females, it is important to properly examine and control for the sex-specific factor of MC phase in order to have a full understanding of brain behavior relationships in translational research.

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bioRxiv2021

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

neuroscience↗

bioRxiv2021

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

neuroscience↗

bioRxiv2023

Bibliometric analysis of neuroscience publications quantifies the impact of data sharing

MotivationNeural morphology, the branching geometry of neurons and glia in the nervous system, is an essential cellular substrate of brain function and pathology. Despite the accelerating production of digital reconstructions of neural morphology in laboratories worldwide, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and prevent researchers from building on others work. Data sharing complements the development of computational resources and literature mining tools to accelerate scientific discovery. ResultsWe carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via the NeuroMorpho.Org online repository leads to a significant increase of citations to the original article, thus directly benefiting the authors. Moreover, the rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative meta-analysis applications, which accrue on average twice the citations of re-analyses of individual datasets. We also designed and deployed an open-source citation tracking web-service that allows researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and the released tool can facilitate the recognition of shared data reuse for promotion and tenure considerations, merit evaluations, and funding decisions. Availability and implementationThe code is available at https://github.com/HerveEmissah/nmo-authors-app (author app) and https://github.com/HerveEmissah/nmo-bibliometric-analysis (bibliometric analysis app).

neuroscience↗

bioRxiv2023

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open source software tools for the creation, visualization, validation and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

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