Field Of Neuroinformatics Research Articles

A comparison of neuroelectrophysiology databases

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

Trainingspace: Neuroeducation Without Borders.

Advancements in the field of neuroinformatics have resulted in a massive explosion of raw data of many varieties, yet many traditional neuroscience training programs have not changed their curricula to reflect the urgent need for improved computational skills that would enable trainees to handle, organize, and interrogate such large, multimodal datasets. Thus, the objective of this project was to build an open access hub of neuroscience educational resources to fill the gap between current neuroscience curricula and the computationally focused skillset required to work with big data. To achieve this aim, we invited representatives from the world's leading neuroscience societies and large-scale brain initiatives to form the INCF Training and Education Committee that would provide oversight over the content and capabilities of the online hub. As a result, we developed TrainingSpace (https://training.incf.org/), an open access hub of nearly 500 multimedia courses, lectures, and tool tutorials covering the subspecialisms of neuroscience and neuroinformatics, as well as computer science, data science, and ethics. In addition to course content, TrainingSpace also provides users with access to publicly available datasets through KnowledgeSpace, a discoverability portal and community encyclopedia for neuroscience, as well as a question and answer forum, Neurostars.org. Since its launch in 2019, TrainingSpace has steadily increased in popularity with both trainees and trainers alike. It has also become popular with content providers that want to make their training materials available to the neuroscience community-at-large, as well as integrate their content into the larger TrainingSpace ecosystem.

MPDB: Molecular Pathways Brain Database.

Molecular Pathways Brain Database (MPDB), is a novel database for molecular information of the brain pathways and is an initiative to provide an organized platform for researchers in the field of neuro-informatics. The database currently has information from 1850 molecules for three different sensory pathways namely olfactory transduction, photo transduction and long-term potentiation. The usefulness of the database is demonstrated by an analysis of the olfactory transduction pathway which helps understand their olfactory specifity and further indicates that some of the molecules have evolved independently among these organisms as per the need of time and function. The database is available for free at http://pranag.physics.iisc.ernet.in/mpdb/Availability:The database is available for free at http://pranag.physics.iisc.ernet.in/mpdb/

GISP Algorithm for identifying strong expression and structural pattern of gene in mouse brain data in the field of neuroinformatics

GISP Algorithm for identifying strong expression and structural pattern of gene in mouse brain data in the field of neuroinformatics

On Neuroinformatics: Mathematical Models of Neuroscience and Neurocomputing

Neuroinformatics is a broad and rapidly evolving discipline concerned with applying information technology and computer science to solve challenges and answer pressing questions in the field of neuroscience. An emerging field of neuroinformatics attempts to model the cellular structures, properties, and functions of neural networks and their applications in neurocomputing and cognitive information systems. This paper presents a set of theories and mathematical models in neuroinformatics for neuroscience and neurocomputing. The neurological foundations of neural clusters and nervous systems are explored. A mathematical treatment of the neurological models and neural signal theories is formally described covering neural signal generation, pulse frequency modulation, and the multiplexer/demultiplexer for neural signal transmissions. Formal models of association, sensory, and motor neurons are established that explain the neurological foundation of applied artificial neural networks. Engineering applications of this neuroinformatics theory and formal neural models are elaborated in cognitive computing, neurocomputing, and neural network analyses.

Ten Years of Neuroinformatics

This issue closes the tenth volume of Neuroinformatics and in this editorial we will present a brief overview of how the journal evolved over its first decade. Of course many outside events sculpted the scientific field during this time. The Human Brain Project was a dominating force in US neuroinformatics when this journal was born, but was discontinued and a new similarly named project may soon dominate European neuroscience and neuroinformatics. In the USA the NIF (Neuroscience Information Framework), the NITRC (Neuroimaging Informatics Tools and Resources Clearinghouse) and many other initiatives became established resources and the INCF (International Neuroinformatics Coordination Facility) with its different National Nodes pushed neuroinformatics throughout the world. Overall it is clear that the field is vigorous and growing fast. Consequently, we also welcomed a number of new competing journals, all of them open access. On its tenth anniversary the journal is healthy and doing well. The increased competition initially hurt us with a decreased paper flow, resulting in a number of lean years (volumes 4–8 had only 250–320 pages each), but we are back to the desired production level (more than 400 pages in a volume) since 2 years. In fact we would have grown more this year, but instead advanced our publication schedule (all issues now appear 2 months earlier during the year). During the lean years we did not relax our editorial standards, with an average acceptance ratio of 40–50 % submitted papers. As a result Neuroinformatics can boast having the largest impact factor in the field, stably fluctuating around 3.0, higher than all computational neuroscience journals, for example. Interestingly, our rejection rate increasingly includes manuscripts that were returned for major revision and then withdrawn by the authors (none in the beginning, up to 25 % of the papers submitted in 2010). In some cases we later discovered the same paper with only small changes in a competing journal. While this may be seen as a bit disrespectful, it seems to reflect a reputation for tough review of resubmissions by the journal, which we only wish to encourage. But more important is scientific content. Did the kind of papers we publish evolve over those 10 years and does this indicate a changing field of neuroinformatics? Some categories were stable, e.g. the majority of papers is about 1 De Schutter E, Ascoli GA, Kennedy DN (2006) On the future of the human brain project. Neuroinform 4: 129–130. 2 Waldrop MM (2012) Brain in a box. Nature 482: 456–458. 3 http://neuinfo.org/ Gupta A, Bug WJ, Marenco LN, Qian X, Condit C, Rangarajan A, Muller HM, Miller PL, Sanders B, Grethe JS, Astakhov V, Shepherd GM, Sternberg PW, Martone ME (2008) Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF). Neuroinform 6: 205–217. 4 http://www.nitrc.org/; Luo X-ZJ, Kennedy DN, Cohen Z (2009) Neuroimaging informatics tools and resources clearinghouse (NITRC) resource announcement. Neuroinform 7: 55–56.

Visiome Platform in Retinal Modeling

Event Abstract Back to Event Visiome Platform in Retinal Modeling Yoshimi Kamiyama1* and Shiro Usui2 1 Aichi Prefectural University, Information Science and Technology, Japan 2 RIKEN Brain Science Institute, Japan Recently computational models and tools are increasingly popularized in Vision science to assist in the exploration and interpretation of complex visual functions and phenomena. There is a critical need for a database where published data can be archived so that they can be accessed, uploaded, downloaded, and tested. Visiome Platform is being developed to answer this need as a web-based database system with a variety of digital research resources in vision science. We have been constructing a website, http://platform.visiome.neuroinf.jp/, which includes contents such as mathematical models, experimental data, illusion designs, visual stimulus generation codes, demonstration movies and analytical software tools.Since reproducibility is a key principle of the scientific method, it is essential that published results be testable by other researchers using the same method. However, for example, most modeling articles do not contain enough information that is necessary for readers to be able to reproduce and verify the results due to lack of initial conditions, incomplete parameter values and so on. Visiome Platform has been designed to make the items reusable. The platform accepts and provides archive files including any formats of model, data or visual stimulus with files of explanatory figures, program sources, readme and other related files.Users can browse Visiome Platform to surf the field of vision science, or seek specific topics of interests. Mathematical models of the vertebrate retina are typical contents in the platform. The retina is an excellent preparation for studying brain structure and function as well as neural signal processing in the brain because of its accessibility and suitability for neuroscientific investigation. Consequently, large amount of research results on all aspects of retinal visual information processing are available. We have been developing mathematical models of retinal neurons and network based on neurophysiological experimental findings. Models we developed are now browsed and downloaded and users can be able to reproduce simulation results in the original papers. In addition to our own models, we have been re-implementing published models of the retina such as a historical model of the photoreceptor and a recent systematic model of the cone-horizontal cell network. Here, we show the mathematical models of the retinal neurons, and demonstrate the electrical responses are quantitatively accounted for by the models in the platform. Visiome Platform now contains a growing collection of the published models of the retina, and the platform supports the field of neuroinformatics by making high-quality retinal models readily available. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Computational neuroscience Citation: Kamiyama Y and Usui S (2019). Visiome Platform in Retinal Modeling. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.111 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 May 2009; Published Online: 09 May 2019. * Correspondence: Yoshimi Kamiyama, Aichi Prefectural University, Information Science and Technology, Aichi, Japan, kamiyama@ist.aichi-pu.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yoshimi Kamiyama Shiro Usui Google Yoshimi Kamiyama Shiro Usui Google Scholar Yoshimi Kamiyama Shiro Usui PubMed Yoshimi Kamiyama Shiro Usui Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Understanding the brain through neuroinformatics

OPINION article Front. Neurosci., 01 August 2008 https://doi.org/10.3389/neuro.01.022.2008

Times of Change, Times of Growth

Feels like ages in the past, the day that the founding of this journal was decided. It wasn’t that long ago, at the beginning of November of 2001, nor an unusual venue, the Society for Neuroscience Meeting in San Diego. But the memory of that meeting is far from the standard way to conduct business these days. Three would-be editors-in-chief (Erik De Schutter, David Kennedy, and myself) shook hands in a bar with Thomas L. Lanigan, the President of the prospective publisher (Humana Press). The encounter was brief and cordial: Mr. Lanigan tackled all substantive issues, but also stressed that his company was a rare example of successful family operation in the biomedical publishing world. Almost to reiterate these points, he was accompanied both by his wife and by the Humana managing editor (in the same spirit, his son, acting as marketing director, later attended the first few meetings of the Editorial Board). We were all certain that neuroinformatics was a field of great promise, and there was great excitement and enthusiasm for the planning of the journal, which would be launched within just 1 year. I had knownMr. Lanigan and the Humana team for quite some time as they publishedmy “Computational Neuroanatomy: Principles and Methods” book, and I knew that they would give us editors uncompromised autonomy and adequate resources to take full and effective responsibility for the scientific charge of the journal. These expectations were not betrayed, and we are truly proud of the long way the journal has come. Thomas Lanigan died last September. That same month, Humana Press was acquired by Springer, a larger publishing company that is extremely well known in the neuroscience and informatics communities. We are grateful to Humana and the Lanigan family for these very successful five years of productive collaboration. At the same time, this merger will open new opportunities for Neuroinformatics. The field is growing, and so is our journal, judging by the respect it commands among fellow scientists and by its top-ranking impact factor. However, further and sustainable growth also requires an increase in manuscript submissions and article flow. This will ensure ever greater visibility without compromising in the least the high scientific standards and the quality of the content. The switch to Springer will ensure wider circulation of our journal. Moreover, by the time this issue appears in press, the new online submission system, based on the widely adopted and familiar Editorial Manager, will be up and running. As always, Neuroinformatics solicits Original Articles, Minireviews, Commentaries, and News Items for consideration and publication. This very issue offers an exciting selection of examples of the broad-spanning field of neuroinformatics, ranging from brain imaging to neuronal morphology, from computational models to knowledge environments. By all means, this is the first journal dedicated to the establishment of the informatics infrastructure of neuroscience. Thomas Lanigan’s enthusiasm continues with our new publisher, and we hope that more and more authors will join in and contribute to developing this most powerful vision of the neuroscience of the future. Neuroinform (2007) 5:95 DOI 10.1007/s12021-007-0005-4

Customizable neuroinformatics database system: XooNIps and its application to the pupil platform

The developing field of neuroinformatics includes technologies for the collection and sharing of neuro-related digital resources. These resources will be of increasing value for understanding the brain. Developing a database system to integrate these disparate resources is necessary to make full use of these resources. This study proposes a base database system termed XooNIps that utilizes the content management system called XOOPS. XooNIps is designed for developing databases in different research fields through customization of the option menu. In a XooNIps-based database, digital resources are stored according to their respective categories, e.g., research articles, experimental data, mathematical models, stimulations, each associated with their related metadata. Several types of user authorization are supported for secure operations. In addition to the directory and keyword searches within a certain database, XooNIps searches simultaneously across other XooNIps-based databases on the Internet. Reviewing systems for user registration and for data submission are incorporated to impose quality control. Furthermore, XOOPS modules containing news, forums schedules, blogs and other information can be combined to enhance XooNIps functionality. These features provide better scalability, extensibility, and customizability to the general neuroinformatics community. The application of this system to data, models, and other information related to human pupils is described here.

Prying Open the Black Box

![Figure][1] If the abstracts for this year's annual meeting of the society for neuroscience (later this month in Atlanta, Georgia) are any indication, collaboration between experimentalists and theoreticians is thriving. This is good news for neuroscience, given that not so long ago, neuroscientists who did experiments the old-fashioned way—recording single-cell activity in living, often behaving experimental animals—had little tolerance for theoreticians of the brain. Both groups hoped to understand how the brain works, but the theoreticians apparently wanted to achieve that goal while ignoring the complex and dynamic ways in which neurons communicate. The pervasive view among the experimentalists was that for the theoreticians, the brain may as well be a black box. Well, that sentiment is no longer pervasive. Rather, computational neuroscience (see the special section in this issue) has made great strides in the past decade, and the field is poised to resolve a spectrum of stubborn mysteries of the brain. ![Figure][1] CREDIT: GETTY IMAGES The sources of the old experimental/theoretical divide are understandable. Most neuroscientists trained in the 1960s, 1970s, and 1980s were comfortable with mathematical representations of basic neuronal phenomena, such as the ionic basis for resting membrane potential. They were awed by the elegant Hodgkin-Huxley equations that explained and predicted the ionic basis for the action potential (based, of course, on an ideal experimental model, the squid giant axon). Experimental neuroscience even embraced the quantal theory of neurotransmitter release by neurons. However, when the theorists began turning their attention to how the cerebellum or hippocampus works, these theories that avoided the complexities of neuronal circuitry and synaptic interactions were suddenly regarded as too speculative and not testable. During the past decade, there has been a robust explosion of methods to characterize the functional and structural details of neurons and their interconnected cellular networks. We can finally realistically simplify the essential features of neuronal ensembles and test theories about what specific neuronal circuits may do. In other words, we can ask why specific cortical pyramidal neurons and interneurons underlie processes such as “edge detection.” A wealth of quantitative data on signaling mechanisms, ion-channel behavior, neuron populations, and morphological features has made possible the development of neuronal and synaptic models that are rigorously constrained by the biochemical and structural properties of specific types of neurons. Such models have wedged open the black box, allowing experimentalists and theoreticians to collaborate. And, as reflected by the Society for Neuroscience meeting agenda, the targets of computational neuroscience are wide-ranging, speaking not only to age-old questions about the possible information encoded in intervals of electrical activity in neurons but also to decision-making and risk/reward assessment and neurological (mainly epilepsy) and psychiatric (schizophrenia and depression) diseases. Computational neuroscience permits us to ask what structural principles explain the selective and redundant wiring diagrams of single-sense pathways and those that integrate multiple senses. Computational neuroscience is also attempting to incorporate the burgeoning field of neuroinformatics and the growing body of electronic databases into its evolution. This is where some of the next great challenges lie. We need to develop and implement uniform, standardized modes of data presentation in neuroscience, so that data from individual research papers can be readily scanned and integrated into more comprehensive databases. This will be fundamental to developing more accurate and useful models of brain function. For many years, the U.S. National Institutes of Health funded the Human Brain Project to nurture this field, and comparable efforts were established globally. However, the funds for ongoing maintenance do not exist, and past investments are endangered.[*][2] If adequate funding over a prolonged period of time is not secured, new principles of education, psychology, and social science that are based on neuroscience are in jeopardy. [1]: pending:yes [2]: #fn-1

Neuroinformatics as a megascience issue.

The greatest increment in acquired knowledge about the brain has occurred in the last two decades, aided by technological advances in molecular biology, molecular genetics, brain imaging, and other new technologies brought about through the computer revolution. The amount of information being generated is increasing in an exponential fashion due to these and ether approaches used by the estimated 50000 neuroscientists working around the globe. These individual investigators working in small groups on highly focused projects publish their results in one of the roughly 200 scientific journals which are published monthly. It is humanly impossible under the current system to integrate all of these data into a meaningful picture of how the brain develops and functions and malfunctions. The complexity and quantity of neuroscience data drives the need to create a global information management system for the neuroscience community. The possibility to do this has already been amply demonstrated by the genome community through bioinformatics. Compared to sequence data, data generated through neuroscience research have many more levels of complexity, including genomic data. This effective demonstration in bioinformatics and the continual advances in the fields of computer and information sciences clearly point to the need for the neuroscience community to embrace and engage the science of informatics, through the global development of the new field of neuroinformatics.