Yacora on the Web (www.yacora.de ) is a web application providing access to collisional radiative models based on the flexible solver Yacora. The main application range is plasma diagnostics in low-pressure plasmas. Available online are three collisional radiative models, namely for atomic hydrogen, molecular hydrogen and helium. This paper gives a brief overview to collisional radiative modeling and to the Yacora solver. The functionality of Yacora on the Web is introduced and the three available models as well as the used input data are presented and discussed. As application example, the models for atomic and molecular hydrogen are applied for investigating spontaneous emission in ionizing and recombining plasmas. This application shows a very good agreement between measured and calculated emission intensities as well as between plasma parameters from other diagnostics and those derived using Yacora on the Web from optical emission spectroscopy results. (C) 2019 Elsevier Ltd. All rights reserved.

This workshop was organized by INCF in Stockholm, Sweden, 18 - 19 June 2012. To ensure that research results can be trusted, it is essential to use methods that are validated. This holds both for analysis and sharing of data, and has become particularly pertinent in the context of the large-scale concerted brain projects that are presently emerging both in Europe and in the US. This workshop brought together scientists concerned with the validation of methods for data analysis from different perspectives. The workshop was motivated by a collaboration involving members of the Norwegian, Polish and German Nodes on establishing a community site for the evaluation of spike-sorting methods (spike.g-node.org). However, the need for validation of data analysis methods is not restricted to spike sorting, but pertains to all physiological and anatomical measurement methods used in neuroscience. Besides spike sorting of extracellular recordings, the workshop addressed the extraction of spikes from two-photon calcium imaging, methods for analysis of local-field potentials, and statistical analysis of spike trains. In addition, management and documentation of analysis workflows, which are crucial for validation of complex multi-stage analyses and thus an essential element of reproducible science, were discussed. An important additional measurement technique, functional magnetic resonance imaging (fMRI), was not considered extensively in the workshop, but it was understood by the workshop participants that the issues, problems and needs identified for the other fields, as well as the conclusions and recommendations, would equally apply to the field of fMRI. Typically, the efforts to define validation procedures for analysis methods, including the collection of benchmark data, start in single labs. However, they should be made available for use in the wide community. As requirements, benchmarks must be: • Broadly accepted by a wide range of laboratories • Available to these laboratories • Easily evaluated The workshop participants discussed what is necessary to bring a validation effort from a prototype-like state, typically achieved in the initiating lab, to a community resource. This process must involve the initiating scientists, but also the community, and ideally an organization that has built up the expertise to support this process. An example of this scheme is the spike-sorting validation project which has been enabled by a close collaboration of the involved scientists with the German INCF Node. The lesson learned from this example is that such a task should be taken on at the scale of an INCF program with expertise and support built up at the secretariat. Discussing the different examples, the workshop made clear that methods validation is at different stages for the various measurement techniques. Thus, specific recommendations differed, but overall the clear picture emerged that there is an unequivocal need for benchmarking activities. The participants agreed on the following overall key recommendations for supporting the various method validation initiatives: 1. Assure development and maintenance of the web site for validation of algorithms for spike sorting of electrical recordings hosted by the G-Node (spike.g-node.org). 2. Use the same technical resources to develop, host and maintain a corresponding website for validation of spike-detection algorithms for calcium imaging data. 3. Develop extensible framework allowing for validation of methods of analysis of other types of data. 4. Involve the community in the development of benchmarks and other means to validate methods for analysis. 5. Initiate and support training activities to educate users in methods validation. 6. Gather experts and users to discuss workflow standardization, and start activities to support reproducibility in data analysis.

Feature linking and segmentation of four stationary patterns are shown to be performed as simultaneous processes by a fully connected, auto-associative neural network of spiking neurons. The patterns have been learned through an asymmetric, Hebbian rule that can handle a varying low activity. In this case the total activity of the patterns ranges between 4 and 7

Event Abstract Back to Event Data Sharing Between NITRC and the INCF Software Center Christian Haselgrove1*, Anders Larsson2, Jan G. Bjaalie3, Janis Breeze2, Robert Buccigrossi4, David Kennedy5, Nina Preuss4 and Raphael Ritz2 1 Neuromorphometrics, Inc, United States 2 INCF, Sweden 3 University of Oslo, Norway 4 TCG, Inc., United States 5 David N. Kennedy Consulting, United States The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, www.nitrc.org), initiated October 2006 through the NIH Blueprint for Neuroscience Research, is a user friendly, web based knowledge environment for the functional magnetic resonance imaging (fMRI) and associated structural analysis community. Through identification of existing tools and resources valuable to this community, NITRC's goal is to develop a knowledge environment to enhance, adopt, distribute, and contribute to the evolution of neuroimaging tools and resources.The INCF Software Center (software.incf.org) was founded as a resource for neuroscience software users and developers. As a web site offering developers tools for distributing software and tracking issues and offering users a way to search for tools appropriate to their needs and provide feedback on the software, the Software Center serves the needs of both groups while connecting the two.While NITRC and the Software Center are complementary, NITRC existing as a community around a wide range of neuroimaging resources and the Software Center focusing on software for the wider neuroscience community, there is significant overlap in the information provided on each. For a large set of core software packages, each site provides general information, documentation, downloads, and opportunity for user feedback. Rather than having two sites cover this common ground, we are sharing this information, allowing developers to provide information to a single source and allowing users to know that the information they find on either site will be consistent and authoritative.NITRC and the Software Center each downloads daily a machine-readable representation of the information hosted by the other, resolves conflicts, and incorporates the information locally. Conflicts are handled at the resource (NITRC) or software package (Software Center) level: entries that are common to both sites are not updated by the remote data. In addition, while the Software Center ignores non-software NITRC resources, NITRC ignores Software Center packages that are not within its scope.Shared entities are imported seamlessly but without duplication of effort. While the summary statement, license, keywords, and other basic information for an imported entry are provided directly by the target site, the documentation, downloads, and user reviews are kept in a single location on the source site but presented on the target site in its standard format. This allows each site to present the common entries in a way consistent to that site while keeping a single, authoritative source for information and data.While NITRC and the INCF Software Center are complementary resources, the common ground between the two is a potential problem, inviting duplication of effort and multiple sources of the same information. Through careful sharing on this common ground, these problems have been mitigated while preserving each site's core focus. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Poster Presentation Topic: Infrastructural and portal services Citation: Haselgrove C, Larsson A, Bjaalie JG, Breeze J, Buccigrossi R, Kennedy D, Preuss N and Ritz R (2019). Data Sharing Between NITRC and the INCF Software Center. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.075 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: 22 May 2009; Published Online: 09 May 2019. * Correspondence: Christian Haselgrove, Neuromorphometrics, Inc, Somerville, United States, ch@neuromorphometrics.com 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 Christian Haselgrove Anders Larsson Jan G Bjaalie Janis Breeze Robert Buccigrossi David Kennedy Nina Preuss Raphael Ritz Google Christian Haselgrove Anders Larsson Jan G Bjaalie Janis Breeze Robert Buccigrossi David Kennedy Nina Preuss Raphael Ritz Google Scholar Christian Haselgrove Anders Larsson Jan G Bjaalie Janis Breeze Robert Buccigrossi David Kennedy Nina Preuss Raphael Ritz PubMed Christian Haselgrove Anders Larsson Jan G Bjaalie Janis Breeze Robert Buccigrossi David Kennedy Nina Preuss Raphael Ritz 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.

The mission of the International Neuroinformatics Coordinating Facility is to coordinate and foster international activities in neuroinformatics. In general, this includes combining neuroscience and informatics research to develop and apply advanced tools and approaches essential for a major advancement in understanding the structure and function of the brain. There are a significant number of resources available for neuroscientists today, yet they are not used as widely as they should because discovering their existence and evaluating their quality and relevance remain tedious tasks. Furthermore, the development of such resources often relies on isolated laboratories where collaboration across projects would be beneficial. INCF has therefore created a Neuroinformatics Portal, and released a Software Center as its first component.

Stimulus-dependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. A simple neural model can reproduce most of the characteristics of these experimental observations based on model neurons having leaky integration and fire-and-reset spikes and with Poisson-distributed, balanced input. The source of the synchrony in the model was common sensory input. The outputs of neurons in the model appear noisy (almost Poisson owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons.

The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical—the hardware, software, and protocols for collaborative research—they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public “open science.” In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience.

The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical--the hardware, software, and protocols for collaborative research--they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public "open science." In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience. Facilitating the widespread sharing of data and tools for neuroscientific research will accelerate the development of neuroinformatics. We propose approaches to overcome the cultural and legal barriers that have slowed these developments to date. We also draw on legal strategies employed by the Free Software community, in suggesting frameworks neuroinformatics might adopt to reinforce the role of public-science databases, and propose a mechanism for identifying and allowing "open science" uses for data whilst still permitting flexible licensing for secondary commercial research.

Following the open source philosophy, neuroscientists are increasingly willing to share primary data as well as custom software with the scientific community. To facilitate this interaction, we are currently establishing a website that links publicly available resources (like experimental data, numerical tools, computer models) and contains extensive annotation. Special emphasis is given to high quality standards of the linked databases and software tools and to integrate both users and providers in this process. The site also contains information about research groups, ongoing activities, links to re- and preprint servers and other interesting resources. In addition, we are exploring web services to see where they could advance the field of neuroinformatics.

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

A model of an associative network of spiking neurons (the Spike Response Model) is used to demonstrate the potential power of the correlation theory for solving the binding problem under biologically realistic constraints. A real world application and the current experimental evidence in support of this hypothesis are discussed.

DeCharms et al.(1995) have provided evidence for stimulus-dependent changes in the correlations between spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. Most of the characteristics of these experimental observations can be reproduced by a simple model based on neurons having leaky integration, fire-and-reset spikes and with Poisson-distributed, balanced input. The source of synchrony in the model was common sensory input. Spike frequency adaptation was implemented by sensory-driven, delayed inhibition. The outputs of neurons in the model appear noisy (almost Poisson) owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons. We address the binding problem and show why synchrony without periodicity might be advantageous in representing multiple objects at the same cortical site simultaneously.

A model of vertical signal flow across a layered cortical structure is presented and analyzed. Neurons communicate through spikes, which evoke an excitatory or inhibitory postsynaptic potential (spike response model). The layers incorporate two anatomical features - dendritic and axonal arborization patterns and distance-dependent time delays. The vertical signal flow through the network is discussed for various stimulus conditions using two different, but typical, axonal arborization patterns. We find stationary as well as oscillatory response, but the oscillatory response may be restricted to a single layer. Confronted with conflicting stimuli the network separates the patterns through phase-shifted oscillations. We also discuss two hypothetical animals, to be called ''cat'' and ''mouse.'' These have different axonal arborizations, which give rise to a different oscillatory response (if any) of the various layers.

1. A model of Ca2+ dynamics in spines of CA1 hippocampal neurons is presented. In contrast to traditional models, which concentrate on the effects of Ca2+ influx, diffusion, buffering, and extrusion, we also consider the additional effect of intracellular Ca2+ stores. 2. It is shown that traditional models without Ca2+ stores cannot account for the time course of long-term potentiation (LTP) induction as found in recent experiments. Experimental data suggest that the intracellular Ca2+ concentration should be elevated for up to 2 s, whereas the Ca2+ concentration in standard models of Ca2+ dynamics decays much faster. 3. When intracellular Ca2+ stores are taken into account, a much slower decay is found. In particular, a model simulation with a stimulation paradigm consisting of two bursts of four impulses at 100 Hz each and variable interburst intervals can reproduce experimental results found for primed or theta-burst stimulation. 4. In our model, Ca2+ release from the store has a nonlinear, bell-shaped dependence on the intracellular Ca2+ concentration, similar to the one found for inositoltrisphosphate and ryanodine receptors. These receptors are known to control calcium release from intracellular stores. 5. Our model suggests an important role of intracellular calcium stores in the induction of LTP. The stores serve as a long-term calcium source that can sustain an intracellular Ca2+ concentration above the resting level for 1-2 s.

As a simple model of the cortical sheet, we study a locally connected net of spiking neurons, Refractoriness, noise, axonal delays, and the time course of excitatory and inhibitory postsynaptic potentials are taken into account explicitly. In addition to a low-activity state and depending on the synaptic efficacy, four different scenarios evolve spontaneously, viz., stripes, spirals, rings, and collective bursts. Our results can be related to experimental observations of drug-induced epilepsy and hallucinations.

We have presented a Hebbian learning rule for spike-processing neural networks. The learning rule enables a network to learn associatively features of an object and to adapt synchronizing connections for object segregation. The learning process is performed with a self-organized switching between the learning and the recall mode. Known patterns are immediately recognized and new patterns are learned. We showed the properties of the learning rule by simulation examples for spatial and spatio-temporal patterns. For the processing of spatio-temporal patterns we chose a short term memory which is constructed of two cascaded leaky integrators. The point of the presented simulation examples was to show that the learning rule fulfills essential requirements for the application of spike-processing to machine vision. Features of an object are learned and recognized by a network without being disturbed by high background activity. When features of two or more objects are presented simultaneously the network separates the various objects in the temporal domain and performs thereby a scene segmentation. For the processing of real world data we have to proceed to multi-layer networks with advanced feature detectors as input units. Finally, a major advantage of the learning rule is that it does not lead to high computation costs and can easily be integrated in our neurocomputer for Spike-processing networks (NESPINN), which is under development.Feature linking via stimulus-evoked oscillations: Experimental results from cat visual cortex and functional implication from a network model",A biologically motivated and analytically soluble model of collective oscillations in the cortex", Biol.Modelling perceptual grouping and figure-ground segregation by means of active reentrant connections", Proc. Natl.