NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D

Eberhard, Jens P.; Wanner, A.; Wittum, Gabriel

doi:10.1016/j.neucom.2006.01.028

Cited by 70 publications

(48 citation statements)

References 26 publications

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“…A simulated neuronal network is obtained with a two-step procedure. The first step utilizes NeuGen [7] to generate a network of N neurons. The second step forms a tree structure with those neurons based on a randomized L-ary tree construction algorithm.…”

Section: Simulation Evaluationmentioning

confidence: 99%

Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks

Suzuki

Boonma

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Neuronal signaling is one of several approaches to network nanomachines in the human body. This paper formulates a noisy optimization problem for a neuronal signaling protocol based on Time Division Multiple Access (TDMA) and solves the problem with a noise-aware optimizer that leverages an evolutionary algorithm. The proposed optimizer is intended to minimize signaling latency by multiplexing and parallelizing signal transmissions in a given neuronal network, while maximizing signaling robustness (i.e., unlikeliness of signal interference). Since latency and robustness objectives conflict with each other, the proposed optimizer seeks the optimal trade-offs between them. It exploits a nonparametric (i.e.. distribution-free) statistical operator because it is not fully known what distribution(s) noise follows in each step/component in neuronal signaling. Simulation results show that the proposed optimizer efficiently obtains quality TDMA signaling schedules and operates a TDMA protocol by balancing conflicting objectives in noisy environments.

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Section: Simulation Evaluationmentioning

confidence: 99%

Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks

Suzuki

Boonma

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…need to be figured out. Tools like NEURON, GENE-SIS, neuroConstruct, and NeuGEN can be used for multicompartment simulation and parametrized synthetic circuit generation/simulation/analysis [61], [62], [63], [64], [65], [66], [67]. Data from the KESM can help narrow down on the range of various parameters for these simulations (see [68] for parameter constraining procedures).…”

Section: Graph Theoretical Analysismentioning

confidence: 99%

Knife-edge scanning microscopy for connectomics research

Choe

Mayerich

Kwon

et al. 2011

The 2011 International Joint Conference on Neural Networks

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In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 µm × 0.7 µm × 1.0 µm can be achieved over ∼1 cm 3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data.

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“…The non-parametric algorithm from Lindsay et al (2007) requires particular preprocessing steps and this leads to a decrease in flexibility and hence specialisation for one cell type. In L-Neuron (Ascoli and Krichmar 2000) and related algorithms such as NeuroPRM (Lien et al 2003) and NeuGen (Eberhard et al 2007), new neuroanatomical rules or correction factors need to be introduced to generate VNs that fit well with a range of different cell types. Consequentially, L-Neuron is not generic because the algorithm needs to be updated to work with new different cell types.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

“…Reconstruction algorithms are descriptive algorithms and often do generate complete morphological descriptions. Our focus in this article is on reconstruction algorithms, in particular on the subclass of so-called sampling algorithms that sample from distributions of morphological measurements (Ascoli and Krichmar 2000;Lien et al 2003;Eberhard et al 2007) to generate a VN. Note that the term 'growth' is used to denote a biologically plausible way of development.…”

Section: Introductionmentioning

confidence: 99%

“…Such knowledge is available for specific cell types only and may not even be valid. Consequently, mixture-model based algorithms can be applied to a limited number of cell types at best (i.e., pyramidal cells (Samsonovich and Ascoli 2005b;Donohue and Ascoli 2005;Eberhard et al 2007), granule cell (Samsonovich and Ascoli 2005a), motor neuron (Burke et al 1992)). The second way to alleviate the simplicity is to use a cell-to-cell reconstruction strategy in which a single neuronal prototype is used instead of a set of neuronal prototypes (as in Samsonovich and Ascoli 2003).…”

Section: Introductionmentioning

confidence: 99%

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Non-parametric Algorithmic Generation of Neuronal Morphologies

2008

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Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-NEURON: and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.

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NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D

Cited by 70 publications

References 26 publications

Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks

Noise-aware evolutionary TDMA optimization for neuronal signaling in medical sensor-actuator networks

Knife-edge scanning microscopy for connectomics research

Non-parametric Algorithmic Generation of Neuronal Morphologies

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