A language to describe the growth of neurites

Hamilton, Patrick

doi:10.1007/bf00200816

Cited by 30 publications

(13 citation statements)

References 26 publications

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“…Particularly, it was exceedingly challenging to translate morphological data into the L-system grammar and vice versa, making it impractical to compare virtual and real neurons (a problem never addressed in an earlier proposal to apply L-systems to neuronal cytoarchitecture). 13 Consequently, we decided to modify the L-system formalism by implementing Hillman's, Tamori's, and Burke's neuroanatomical algorithms instead of Lyndenmayer's formal rewrite rule. The resulting ''modified L-systems,'' called L-Neuron, 3 continues to make use of the turtle graphic interpretation of L-system strings, 28 and integrates the three adopted algorithms with a full account of branch angles (Fig.…”

Section: Modeling Dendritic Morphologymentioning

confidence: 99%

Progress and perspectives in computational neuroanatomy

Ascoli

1999

Anat. Rec.

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The tremendous increase in processing power of personal computers has recently allowed the construction of highly sophisticated models of neuronal function and behavior. Anatomy plays a fundamental role in supporting and shaping nervous system activity, yet to date most details of such a role have escaped the efforts of experimental and theoretical neuroscientists, mainly because of the problem's complexity. When accurate cellular morphologies are included in electrophysiological computer simulations, quantitative and qualitative effects of dendritic structure on firing properties can be extensively characterized. Complete models of dendritic morphology can be implemented to allow the computer generation of virtual neurons that model the anatomical characteristics of their real counterparts to a great degree of approximation. From a restricted and already available experimental database, stochastic and statistical algorithms can create an unlimited number of non-identical virtual neurons within several mammalian morphological classes, storing them in a compact and parsimonious format. When modeled neurons are distributed in three-dimensional and biologically plausible rules governing axonal navigation and connectivity are added to the simulations, entire portions of the nervous system can be "grown" as anatomically realistic neural networks. These computational constructs are useful to determine the influence of local geometry on system neuroanatomy, and to investigate systematically the mutual interactions between anatomical parameters and electrophysiological activity at the network level. A detailed computer model of a "virtual brain" that was truly equivalent to the biological structure could in principle allow scientists to carry out experiments that could not be performed on real nervous systems because of physical constraints. The computational approach to neuroanatomy is just at its beginning, but has a great potential to enhance the intuition of investigators and to aid neuroscience education. Anat Rec (New Anat): 257:195-207, 1999.

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Section: Modeling Dendritic Morphologymentioning

confidence: 99%

Progress and perspectives in computational neuroanatomy

Ascoli

1999

Anat. Rec.

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“…Maybe such an approach can be extended in the future to simulate metabolic networks of individual neurons (Ravasz et al, 2002;Strogatz, 2001). Similar simulations have been published (Ascoli, 1999;Ascoli et al, 2001;Burke et al, 1992;Bush and Sejnowski, 1993;Hamilton, 1993;Senft and Ascoli, 1999). These models can be used for a deeper understanding of the normal and pathologic processing of neuronal information on large scales close to functionally relevant biological microspace.…”

Section: Resultsmentioning

confidence: 68%

Videomicroscopy, image processing, and analysis of whole histologic sections of the human brain

Schmitt¹,

Eggers²,

Modersitzki³

2005

Microsc. Res. Tech.

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Serial histologic sections of a whole human brain may have extensions of up to 130 x 130 mm within the coronal plane around the temporal lobe. To date, however, technology has not provided a bright field microscope that is able to shift the object holder continuously in the x- and y-direction over such distances and still possess the same optical capabilities as comparable devices. We developed a new light microscope to continuously quantify such sections. We also developed the computing environment for controlling the device and for analyzing the data produced. In principle, we are now able to quantify each neuron of a human brain. The data ultimately will provide the most detailed structural information about the human brain ascertained thus far. Such detailed information of the spatial distribution of neurons is essential to develop realistic models for simulation of large-scale neuronal networks and to investigate the significance of neuronal arrangements with respect to neuronal signal processing in the CNS. After preprocessing of the data produced by the new microscope, we are able to detect lamination patterns in the spatial distribution of gravity centers of cells. Furthermore, morphological features like size of the projection area and mean staining intensity are visualized as a particle process. The particle process presents the sizes and staining intensity of perikaryons and allows a distinction of gray matter and white matter. These results provide evidence that the system works correctly and can be applied to a systematic analysis of a larger sequence of serial histologic sections. The objective of this study is to introduce the very large section analyzing microscope (VLSAM) and to present the initial data produced by the system. Moreover, we will discuss workload and future developments of the parallel image analysis system that are associated with the microscope.

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“…The L-systems are the parallel rewriting systems that successively and recursively enable the replacement parts of a simple object [50]. It has been used to simulate the development of a branching structure such as a tree [51] and neuronal dendritic arborizations [52,53]. As the extension of the L-systems, the open L-systems contain a computational module to simulate the communication between the object and its environment.…”

Section: Computational Modeling Of Nerve Growth and Pathfindingmentioning

confidence: 99%

Axon guidance and neuronal migration research in China

Yuan

2010

Sci. China Life Sci.

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Proper migration of neuronal somas and axonal growth cones to designated locations in the developing brain is essential for the assembly of functional neuronal circuits. Rapid progress in research of axon guidance and neuronal migration has been made in the last twenty years. Chinese researchers began their exploration in this field ten years ago and have made significant contributions in clarifying the signal transduction of axon guidance and neuronal migration. Several unique experimental approaches, including the migration assay of single isolated neurons in response to locally delivered guidance cues, have been developed by Chinese neuroscientists to investigate the molecular machinery underlying these guidance events.

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A language to describe the growth of neurites

Cited by 30 publications

References 26 publications

Progress and perspectives in computational neuroanatomy

Progress and perspectives in computational neuroanatomy

Videomicroscopy, image processing, and analysis of whole histologic sections of the human brain

Axon guidance and neuronal migration research in China

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