scholarly journals Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic era

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Aurel A Lazar ◽  
Tingkai Liu ◽  
Mehmet Kerem Turkcan ◽  
Yiyin Zhou
Keyword(s):  
Computational Models ◽  
Fruit Fly ◽  
Neuron Activity ◽  
Activity Data ◽  
Computing Platform ◽  
Brain Circuits ◽  
Entry Points ◽  
Functional Logic ◽  
Drosophila Brain ◽  
3D Exploration

In recent years, a wealth of Drosophila neuroscience data have become available including cell type, connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison and evaluation of circuit functions of the fruit fly brain.

scholarly journals FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Drosophila Brain in the Connectomic/Synaptomic Era

2020 ◽  
Author(s):  
Aurel A. Lazar ◽  
Tingkai Liu ◽  
Mehmet Kerem Turkcan ◽  
Yiyin Zhou
Keyword(s):  
Computational Models ◽  
Neural Circuits ◽  
Developmental Stages ◽  
Fruit Fly ◽  
Central Complex ◽  
Neuron Activity ◽  
Activity Data ◽  
Entry Points ◽  
Functional Logic ◽  
Brain Data

AbstractIn recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab.FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of seemingly disparate databases, arrays and tables, to a well structured data and executable circuit repository.The FlyBrainLab User Interface supports a highly intuitive and automated work-flow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data. Furthermore, efficient comparisons of circuit models are supported, across models developed by different researchers, across different developmental stages of the fruit fly and across different datasets.The FlyBrainLab Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory systems.Seeking to transcend the limitations of the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

scholarly journals A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

2021 ◽  
Author(s):  
Aurel A Lazar ◽  
Mehmet Kerem Turkcan ◽  
Yiyin Zhou
Keyword(s):  
Web Application ◽  
Antennal Lobe ◽  
Fruit Fly ◽  
Brain Regions ◽  
Feedback Loops ◽  
Free Form ◽  
Brain Anatomy ◽  
Computing Platform ◽  
Functional Logic ◽  
Drosophila Brain

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain at this scale posits significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for defining functional circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of massive local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

scholarly journals The Fruit Fly Brain Observatory: From Structure to Function

2019 ◽  
Author(s):  
Nikul H. Ukani ◽  
Chung-Heng Yeh ◽  
Adam Tomkins ◽  
Yiyin Zhou ◽  
Dorian Florescu ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Research ◽  
Model Organism ◽  
Fruit Fly ◽  
Data Repositories ◽  
Brain Circuits ◽  
Multiple Data ◽  
Computer Scientists ◽  
Brain Data ◽  
The Brain

AbstractThe fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Neuroglial Behaviour in Computer Science

2006 ◽  
pp. 1-21 ◽  
Author(s):  
Ana B. Porto ◽  
Alejandro Pazos
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Computer Science ◽  
Glial Cells ◽  
Computational Models ◽  
Brain Circuits

This chapter presents a study that incorporates into the connectionist systems new elements that emulate cells of the glial system. More concretely, we have considered a determined category of glial cells known as astrocytes, which are believed to be directly implicated in the brain’s information processing. Computational models have helped to provide a better understanding of the causes and factors that are involved in the specific functioning of particular brain circuits. The present work will use these new insights to progress in the field of computing sciences and artificial intelligence. The proposed connectionist systems are called artificial neuroglial networks (ANGN).

scholarly journals It Takes Two: Dimerization Is Essential for the Broad-Spectrum Predatory and Defensive Activities of the Venom Peptide Mp1a from the Jack Jumper Ant Myrmecia pilosula

Biomedicines ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 185 ◽  
Author(s):  
Samantha A. Nixon ◽  
Zoltan Dekan ◽  
Samuel D. Robinson ◽  
Shaodong Guo ◽  
Irina Vetter ◽  
...  
Keyword(s):  
Broad Spectrum ◽  
Haemonchus Contortus ◽  
Parasitic Nematode ◽  
Biological Activities ◽  
Fruit Fly ◽  
Cell Types ◽  
Activity Data ◽  
Chemical Complexity ◽  
Rational Engineering ◽  
Structure Activity

Ant venoms have recently attracted increased attention due to their chemical complexity, novel molecular frameworks, and diverse biological activities. The heterodimeric peptide ∆-myrtoxin-Mp1a (Mp1a) from the venom of the Australian jack jumper ant, Myrmecia pilosula, exhibits antimicrobial, membrane-disrupting, and pain-inducing activities. In the present study, we examined the activity of Mp1a and a panel of synthetic analogues against the gastrointestinal parasitic nematode Haemonchus contortus, the fruit fly Drosophila melanogaster, and for their ability to stimulate pain-sensing neurons. Mp1a was found to be both insecticidal and anthelmintic, and it robustly activated mammalian sensory neurons at concentrations similar to those reported to elicit antimicrobial and cytotoxic activity. The native antiparallel Mp1a heterodimer was more potent than heterodimers with alternative disulfide connectivity, as well as monomeric analogues. We conclude that the membrane-disrupting effects of Mp1a confer broad-spectrum biological activities that facilitate both predation and defense for the ant. Our structure–activity data also provide a foundation for the rational engineering of analogues with selectivity for particular cell types.

scholarly journals Meox1 accelerates myocardial hypertrophic decompensation through Gata4

2017 ◽  
Vol 114 (2) ◽  
pp. 300-311 ◽  
Author(s):  
Dan Lu ◽  
Jizheng Wang ◽  
Jing Li ◽  
Feifei Guan ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Pressure Overload ◽  
Digital Gene Expression ◽  
Familial Hypertrophic Cardiomyopathy ◽  
Mouse Heart ◽  
Activity Data ◽  
Aberrant Expression ◽  
Downstream Target ◽  
Pathological Hypertrophy ◽  
Entry Points

AbstractAimsPathological hypertrophy is the result of gene network regulation, which ultimately leads to adverse cardiac remodelling and heart failure (HF) and is accompanied by the reactivation of a ‘foetal gene programme’. The Mesenchyme homeobox 1 (Meox1) gene is one of the foetal programme genes. Meox1 may play a role in embryonic development, but its regulation of pathological hypertrophy is not known. Therefore, this study investigated the effect of Meox1 on pathological hypertrophy, including familial and pressure overload-induced hypertrophy, and its potential mechanism of action.Methods and resultsMeox1 expression was markedly down-regulated in the wild-type adult mouse heart with age, and expression was up-regulated in heart tissues from familial dilated cardiomyopathy (FDCM) mice of the cTnTR141W strain, familial hypertrophic cardiomyopathy (FHCM) mice of the cTnTR92Q strain, pressure overload-induced HF mice, and hypertrophic cardiomyopathy (HCM) patients. Echocardiography, histopathology, and hypertrophic molecular markers consistently demonstrated that Meox1 overexpression exacerbated the phenotypes in FHCM and in mice with thoracic aorta constriction (TAC), and that Meox1 knockdown improved the pathological changes. Gata4 was identified as a potential downstream target of Meox1 using digital gene expression (DGE) profiling, real-time PCR, and bioinformatics analysis. Promoter activity data and chromatin immunoprecipitation (ChIP) and Gata4 knockdown analyses indicated that Meox1 acted via activation of Gata4 transcription.ConclusionMeox1 accelerated decompensation via the downstream target Gata4, at least in part directly. Meox1 and other foetal programme genes form a highly interconnected network, which offers multiple therapeutic entry points to dampen the aberrant expression of foetal genes and pathological hypertrophy.

scholarly journals From heterogeneous datasets to predictive models of embryonic development

2021 ◽  
Author(s):  
Sayantan Dutta ◽  
Aleena L. Patel ◽  
Shannon E. Keenan ◽  
Stanislav Y. Shvartsman
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Computational Models ◽  
Fruit Fly ◽  
Computational Approach ◽  
Data Driven ◽  
Gene Repression ◽  
Future Studies ◽  
Heterogeneous Datasets ◽  
Quantitative Results ◽  
Genome Manipulation

AbstractModern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing, and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here we use data from our work on signal-dependent gene repression in the fruit fly, Drosophila melanogaster, to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation, and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

scholarly journals Confidence estimation as a stochastic process in a neurodynamical system of decision making

2015 ◽  
Vol 114 (1) ◽  
pp. 99-113 ◽  
Author(s):  
Ziqiang Wei ◽  
Xiao-Jing Wang
Keyword(s):  
Task Difficulty ◽  
Single Neuron ◽  
Neural Mechanism ◽  
Neuron Activity ◽  
Confidence Estimation ◽  
Decision Under Uncertainty ◽  
Activity Data ◽  
Spiking Network ◽  
Confidence Assessment ◽  
Single Neuron Activity

Evaluation of confidence about one's knowledge is key to the brain's ability to monitor cognition. To investigate the neural mechanism of confidence assessment, we examined a biologically realistic spiking network model and found that it reproduced salient behavioral observations and single-neuron activity data from a monkey experiment designed to study confidence about a decision under uncertainty. Interestingly, the model predicts that changes of mind can occur in a mnemonic delay when confidence is low; the probability of changes of mind increases (decreases) with task difficulty in correct (error) trials. Furthermore, a so-called “hard-easy effect” observed in humans naturally emerges, i.e., behavior shows underconfidence (underestimation of correct rate) for easy or moderately difficult tasks and overconfidence (overestimation of correct rate) for very difficult tasks. Importantly, in the model, confidence is computed using a simple neural signal in individual trials, without explicit representation of probability functions. Therefore, even a concept of metacognition can be explained by sampling a stochastic neural activity pattern.

scholarly journals A rewrite-based computational model for functional logic programming

10.29007/3ks9 ◽  
2018 ◽  
Author(s):  
Mircea Marin ◽  
Temur Kutsia ◽  
Besik Dundua
Keyword(s):  
Computational Model ◽  
Logic Programming ◽  
Computational Models ◽  
Suitable Model ◽  
Logic Programs ◽  
A Value ◽  
Term Rewrite Systems ◽  
Functional Logic Programming ◽  
Functional Logic ◽  
Core Language

Functional logic programming extends the functional programming style with two important features: the possibility to define nondeterministic operations with overlapping rules, and the usage of logic variables in both defining rules and expressions to evaluate. Conditional constructor-based term rewrite systems (CB-CTRSs) emerged as a suitable model for functional logic programs, because they can be easily transformed into an equivalent program in a core language where computations can be performed more efficiently.We consider a recent proposal by Antoy and Hanus, of translating CB-CTRSs into an equivalent class of programs where computation can be performed by mere rewriting. His computational model has the limitation of computing only ground answer substitutions for equations with strict semantics interpreted as joinability to a value.We propose two adjustments of their computational models, which are capable to compute non-ground answers.

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