neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space

Gleeson, Padraig; Steuber, Volker; Silver, R. Angus

doi:10.1016/j.neuron.2007.03.025

Cited by 193 publications

(167 citation statements)

References 71 publications

(50 reference statements)

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“…The main usage and potential applications of NeuroMorpho.Org include comparative morphological and stereological analysis, compartmental simulations of neuronal electrophysiology, computational models of structure and development, scientific education and illustration, large-scale data mining, and anatomically realistic neural networks (Stepanyants and Chklovskii, 2005;Samsonovich and Ascoli, 2006;Gleeson et al, 2007). Examples of "success stories" in the re-use of neuronal reconstructions are particularly abundant in computational neuroscience, where biophysical studies are increasingly based on accurate morphological data (Migliore et al, 2003).…”

Section: Neuromorphoorg Data Contentmentioning

confidence: 99%

NeuroMorpho.Org: A Central Resource for Neuronal Morphologies

Ascoli¹,

Donohue²,

Halavi³

2007

J. Neurosci.

660

613

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Section: Neuromorphoorg Data Contentmentioning

confidence: 99%

NeuroMorpho.Org: A Central Resource for Neuronal Morphologies

Ascoli¹,

Donohue²,

Halavi³

2007

J. Neurosci.

660

613

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“…Examples include L-Neuron (104,116) (running local algorithms independent of possible extrinsic constraints) and ArborVitae (116,117) (not yet available but implementing global algorithms in which populations of neurons grow based on environmental constraints). Other tools for the generation of networks of neurons closely matching the morphology and connectivity of different brain regions include NeuGen (106), neuroConstruct (107), and NET-MORPH (105).…”

Section: Generation Toolsmentioning

confidence: 99%

Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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“…In our opinion, mathematical toy models will continue to play a major role in guiding the way we think about neuroscience. However, in order to avoid that the biggest problems addressed in computational neuroscience are limited to the size of one PhD project, initiatives for shared modular and reusable code and standardized simulator interfaces [110][111][112][113] are important. More generally, the community of theoretical and computational neuroscience would profit from a simulation environment where the ideas developed in the toy models could be tested on a larger scale, in a biologically plausible setting, and where the ideas arising in different communities and labs are finally connected to the bigger whole.…”

mentioning

confidence: 99%

Theory and Simulation in Neuroscience

Gerstner

Sprekeler

Deco

2012

Science

159

111

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Abstract:Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.Mathematical and computational approaches in neuroscience have a long tradition that can be followed back to early mathematical theories of perception [1,2] and of current integration by a neuronal cell membrane [3]. Hodgkin and Huxley combined their experiments with a mathematical description, which they used for simulations on one of the early computers [4]. Hebb's ideas on assembly formation [5] have, already in 1956, inspired simulations on the largest computers available at that time [6]. Since the 1980ies the field of theoretical and computational neuroscience has grown enormously [7].Modern neuroscience methods requiring extensive training have led to a specialization of researchers so that neuroscience today is fragmented into labs working on genes and molecules; on single-cell electrophysiology; on multi-neuron recordings; on cognitive neuroscience and psychophysics, to name just a few. One of the central tasks of computational neuroscience is to bridge these different levels of description by simulation and mathematical theory. The bridge can be built in two different directions. Bottom-up models integrate what is known on a lower level (e.g., properties of ion channels) to explain phenomena observed on a higher level (e.g., generation of action potentials [4,[8][9][10]). Top-down models, on the other hand, start with known cognitive functions of the brain (e.g., working memory), and deduce from these how components (e.g., neurons or groups of neurons) should behave to achieve these functions. Influential examples of the top-down approach are theories of associative memories [11,12], reinforcement learning [13,14], and sparse coding [15,16].Bottom-up and top-down models can either be studied by mathematical theory (theoretical neuroscience) or by computer simulations (computational neuroscience). Theory has the advantage of providing a complete picture of the model behavior for all possible parameter settings, but analytical solutions are restricted to relatively simple models. The aim of theory is therefore to purify biological ideas to the bare minimum, so as to arrive at a 'toy model', which crystallizes a concept in a set of mathematical equations that can be fully understood. Simulations, in contrast, can be applied to all models, simplified as well as complex ones, but they can only sample the model behavior for a...

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neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space

Cited by 193 publications

References 71 publications

NeuroMorpho.Org: A Central Resource for Neuronal Morphologies

NeuroMorpho.Org: A Central Resource for Neuronal Morphologies

Neuron tracing in perspective

Theory and Simulation in Neuroscience

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