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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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2
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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3
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Korom M, Camacho MC, Filippi CA, Licandro R, Moore LA, Dufford A, Zöllei L, Graham AM, Spann M, Howell B, Shultz S, Scheinost D. Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies. Dev Cogn Neurosci 2021; 53:101055. [PMID: 34974250 PMCID: PMC8733260 DOI: 10.1016/j.dcn.2021.101055] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/28/2021] [Accepted: 12/26/2021] [Indexed: 01/07/2023] Open
Abstract
The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT’NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging. The field of infant MRI is young with evolving standards. We address 20 questions that researchers commonly receive reviewers. These come from research ethics boards, grant, and manuscript reviewers. This article reflects the cumulative knowledge of experts in the FIT’NG community.
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Affiliation(s)
- Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.
| | - M Catalina Camacho
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University School of Medicine, St. Louis, MO, USA.
| | - Courtney A Filippi
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Roxane Licandro
- Institute of Visual Computing and Human-Centered Technology, Computer Vision Lab, TU Wien, Vienna, Austria; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research, Medical University of Vienna, Vienna, Austria
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Alexander Dufford
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Marisa Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Department of Human Development and Family Science, Virginia Polytechnic Institute and State University, Roanoke, VA, USA
| | | | - Sarah Shultz
- Division of Autism & Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA.
| | - Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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Mustaqeem M, Saqib M. Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection. CLUSTER COMPUTING 2021; 24:2581-2595. [PMID: 33880074 PMCID: PMC8050160 DOI: 10.1007/s10586-021-03282-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 03/11/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane.
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Affiliation(s)
- Mohd. Mustaqeem
- CSE Department, Institute of Technology & Management (A.K.T.U), Aligarh, U.P India
| | - Mohd. Saqib
- Mathematic and Computing Department, Indian Institute of Technology (ISM), Dhanbad, Jharkhand India
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5
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Mangul S, Mosqueiro T, Abdill RJ, Duong D, Mitchell K, Sarwal V, Hill B, Brito J, Littman RJ, Statz B, Lam AKM, Dayama G, Grieneisen L, Martin LS, Flint J, Eskin E, Blekhman R. Challenges and recommendations to improve the installability and archival stability of omics computational tools. PLoS Biol 2019; 17:e3000333. [PMID: 31220077 PMCID: PMC6605654 DOI: 10.1371/journal.pbio.3000333] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/02/2019] [Indexed: 01/07/2023] Open
Abstract
Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through uniform resource locators (URLs) published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed "easy to install," and 28% of the tools failed to be installed at all because of problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software.
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Affiliation(s)
- Serghei Mangul
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Thiago Mosqueiro
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Richard J. Abdill
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Dat Duong
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Keith Mitchell
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Varuni Sarwal
- Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Brian Hill
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jaqueline Brito
- Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil
| | - Russell Jared Littman
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Benjamin Statz
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Angela Ka-Mei Lam
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Gargi Dayama
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Laura Grieneisen
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Lana S. Martin
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Ran Blekhman
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, United States of America
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Minnesota, United States of America
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6
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Gleeson P, Lung D, Grosu R, Hasani R, Larson SD. c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0379. [PMID: 30201842 PMCID: PMC6158223 DOI: 10.1098/rstb.2017.0379] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2018] [Indexed: 01/03/2023] Open
Abstract
The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode Caenorhabditis elegans. A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined. For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools developed in the OpenWorm modelling toolchain. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - David Lung
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
| | - Radu Grosu
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
| | - Ramin Hasani
- Cyber-Physical Systems, Technische Universität Wien, Vienna, Austria
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7
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Sanz-Leon P, Robinson PA, Knock SA, Drysdale PM, Abeysuriya RG, Fung FK, Rennie CJ, Zhao X. NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics. PLoS Comput Biol 2018; 14:e1006387. [PMID: 30133448 PMCID: PMC6122812 DOI: 10.1371/journal.pcbi.1006387] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 09/04/2018] [Accepted: 07/22/2018] [Indexed: 01/02/2023] Open
Abstract
A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.
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Affiliation(s)
- Paula Sanz-Leon
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | - Peter A. Robinson
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | - Stuart A. Knock
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
| | | | - Romesh G. Abeysuriya
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Felix K. Fung
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
- Downstate Medical Center, State University of New York, Brooklyn, New York, United States of America
| | | | - Xuelong Zhao
- School of Physics, University of Sydney, Sydney, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, Australia
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8
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Jȩdrzejewski-Szmek Z, Abrahao KP, Jȩdrzejewska-Szmek J, Lovinger DM, Blackwell KT. Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes. Front Neuroinform 2018; 12:47. [PMID: 30108495 PMCID: PMC6079282 DOI: 10.3389/fninf.2018.00047] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/06/2018] [Indexed: 11/25/2022] Open
Abstract
Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. Optimizations reached convergence within 1,600-4,000 model evaluations (200-500 generations × population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.
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Affiliation(s)
| | - Karina P. Abrahao
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | | | - David M. Lovinger
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | - Kim T. Blackwell
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States
- Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States
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9
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Siegle JH, López AC, Patel YA, Abramov K, Ohayon S, Voigts J. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J Neural Eng 2018; 14:045003. [PMID: 28169219 DOI: 10.1088/1741-2552/aa5eea] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Closed-loop experiments, in which causal interventions are conditioned on the state of the system under investigation, have become increasingly common in neuroscience. Such experiments can have a high degree of explanatory power, but they require a precise implementation that can be difficult to replicate across laboratories. We sought to overcome this limitation by building open-source software that makes it easier to develop and share algorithms for closed-loop control. APPROACH We created the Open Ephys GUI, an open-source platform for multichannel electrophysiology experiments. In addition to the standard 'open-loop' visualization and recording functionality, the GUI also includes modules for delivering feedback in response to events detected in the incoming data stream. Importantly, these modules can be built and shared as plugins, which makes it possible for users to extend the functionality of the GUI through a simple API, without having to understand the inner workings of the entire application. MAIN RESULTS In combination with low-cost, open-source hardware for amplifying and digitizing neural signals, the GUI has been used for closed-loop experiments that perturb the hippocampal theta rhythm in a phase-specific manner. SIGNIFICANCE The Open Ephys GUI is the first widely used application for multichannel electrophysiology that leverages a plugin-based workflow. We hope that it will lower the barrier to entry for electrophysiologists who wish to incorporate real-time feedback into their research.
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Affiliation(s)
- Joshua H Siegle
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109, United States of America, jsiegle on GitHub
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10
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Williams JJ, Teal TK. A vision for collaborative training infrastructure for bioinformatics. Ann N Y Acad Sci 2016; 1387:54-60. [PMID: 27603332 DOI: 10.1111/nyas.13207] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 07/14/2016] [Accepted: 07/20/2016] [Indexed: 11/29/2022]
Abstract
In biology, a missing link connecting data generation and data-driven discovery is the training that prepares researchers to effectively manage and analyze data. National and international cyberinfrastructure along with evolving private sector resources place biologists and students within reach of the tools needed for data-intensive biology, but training is still required to make effective use of them. In this concept paper, we review a number of opportunities and challenges that can inform the creation of a national bioinformatics training infrastructure capable of servicing the large number of emerging and existing life scientists. While college curricula are slower to adapt, grassroots startup-spirited organizations, such as Software and Data Carpentry, have made impressive inroads in training on the best practices of software use, development, and data analysis. Given the transformative potential of biology and medicine as full-fledged data sciences, more support is needed to organize, amplify, and assess these efforts and their impacts.
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Affiliation(s)
- Jason J Williams
- DNA Learning Center, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
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11
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Meyer R, Obermayer K. pypet: A Python Toolkit for Data Management of Parameter Explorations. Front Neuroinform 2016; 10:38. [PMID: 27610080 PMCID: PMC4996826 DOI: 10.3389/fninf.2016.00038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 08/09/2016] [Indexed: 11/16/2022] Open
Abstract
pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
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Affiliation(s)
- Robert Meyer
- Neuroinformatics Group, Department of Software Engineering and Theoretical Computer Science, Technical University BerlinBerlin, Germany; Bernstein Center for Computational NeuroscienceBerlin, Germany
| | - Klaus Obermayer
- Neuroinformatics Group, Department of Software Engineering and Theoretical Computer Science, Technical University Berlin Berlin, Germany
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12
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Almog M, Korngreen A. Is realistic neuronal modeling realistic? J Neurophysiol 2016; 116:2180-2209. [PMID: 27535372 DOI: 10.1152/jn.00360.2016] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/17/2016] [Indexed: 11/22/2022] Open
Abstract
Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.
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Affiliation(s)
- Mara Almog
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and.,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Alon Korngreen
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel; and .,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
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13
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Sarma GP, Jacobs TW, Watts MD, Ghayoomie SV, Larson SD, Gerkin RC. Unit testing, model validation, and biological simulation. F1000Res 2016; 5:1946. [PMID: 27635225 PMCID: PMC5007758 DOI: 10.12688/f1000research.9315.1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/02/2016] [Indexed: 11/24/2022] Open
Abstract
The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in
OpenWorm, an open-science project aimed at modeling
Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.
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Affiliation(s)
- Gopal P Sarma
- School of Medicine, Emory University, Atlanta, USA.,OpenWorm Foundation, Boston, USA
| | - Travis W Jacobs
- Department of Bioengineering, Imperial College London, London, UK.,OpenWorm Foundation, Boston, USA
| | - Mark D Watts
- The University of Texas at Austin, Austin, USA.,OpenWorm Foundation, Boston, USA
| | - S Vahid Ghayoomie
- Laboratory of Systems Biology and Bioinformatics, University of Tehran, Tehran, Iran.,OpenWorm Foundation, Boston, USA
| | | | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, USA.,OpenWorm Foundation, Boston, USA
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14
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Stockton DB, Santamaria F. NeuroManager: a workflow analysis based simulation management engine for computational neuroscience. Front Neuroinform 2015; 9:24. [PMID: 26528175 PMCID: PMC4602303 DOI: 10.3389/fninf.2015.00024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 09/22/2015] [Indexed: 11/13/2022] Open
Abstract
We developed NeuroManager, an object-oriented simulation management software engine for computational neuroscience. NeuroManager automates the workflow of simulation job submissions when using heterogeneous computational resources, simulators, and simulation tasks. The object-oriented approach (1) provides flexibility to adapt to a variety of neuroscience simulators, (2) simplifies the use of heterogeneous computational resources, from desktops to super computer clusters, and (3) improves tracking of simulator/simulation evolution. We implemented NeuroManager in MATLAB, a widely used engineering and scientific language, for its signal and image processing tools, prevalence in electrophysiology analysis, and increasing use in college Biology education. To design and develop NeuroManager we analyzed the workflow of simulation submission for a variety of simulators, operating systems, and computational resources, including the handling of input parameters, data, models, results, and analyses. This resulted in 22 stages of simulation submission workflow. The software incorporates progress notification, automatic organization, labeling, and time-stamping of data and results, and integrated access to MATLAB's analysis and visualization tools. NeuroManager provides users with the tools to automate daily tasks, and assists principal investigators in tracking and recreating the evolution of research projects performed by multiple people. Overall, NeuroManager provides the infrastructure needed to improve workflow, manage multiple simultaneous simulations, and maintain provenance of the potentially large amounts of data produced during the course of a research project.
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Affiliation(s)
- David B. Stockton
- Biomedical Engineering Program, The University of Texas at San AntonioSan Antonio, TX, USA
| | - Fidel Santamaria
- UTSA Neurosciences Institute, The University of Texas at San AntonioSan Antonio, TX, USA
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15
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Marin B, Gleeson P, Cantarelli M, Cannon RC, Silver RA. Automated code generation from LEMS, the general purpose model specification language underpinning NeuroML2. BMC Neurosci 2014. [PMCID: PMC4126487 DOI: 10.1186/1471-2202-15-s1-p45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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