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Objective Multi-variable Classification and Inference of Biological Neuronal Networks.

TL;DR: This research develops a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons and sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.
Abstract: Classification of biological neuron types and networks poses challenges to the full understanding of the brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons. This presents advantages against the existing approaches since the mutual information or the delay between neurons obtained from spike trains are more abundant data compare to conventional morphological data. We firstly designed two open-access supporting computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then we investigate how the concept of network tomography could be achieved with cortical neuronal circuits for morphological, topological and electrical classification of neurons. We extract the simulated data to many different classifiers (including SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying the specific cell type (and sub-group types) achieving accuracies of up to 70\%. Inference of biological network structures using network tomography reached up to 65\% of accuracy. We also analysed recall, precision and F1score of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our research not only contributes to existing classification efforts but sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.
Citations
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Journal ArticleDOI

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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

References
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Book ChapterDOI

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01 Jan 2012

139,059 citations


"Objective Multi-variable Classifica..." refers background in this paper

  • ...Besides, this type of information is crucial for not only neuron type target drugs but to precise micro-scale brain-machine interfaces that can interact at the cellular level to uncover small-scale information in the brain [4], [5]....

    [...]

Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations


"Objective Multi-variable Classifica..." refers background in this paper

  • ...Even though these new approaches are exciting, we hypothesise that based on the relationship of cellular morphology and activity [7], we can perform reliable objective morphological classification based on cellular activity and communication alone....

    [...]

Book
01 Jan 1983
TL;DR: Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected.
Abstract: Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected. Such methods, which are also k ...

2,960 citations

Journal ArticleDOI
TL;DR: This work presents the basic ideas that would help informed users make the most efficient use of NEURON, the powerful and flexible environment for implementing models of individual neurons and small networks of neurons.
Abstract: The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is needed to test hypotheses about the mechanisms that govern these signals and how nervous system function emerges from the operation of these mechanisms. The NEURON simulation program provides a powerful and flexible environment for implementing such models of individual neurons and small networks of neurons. It is particularly useful when membrane potential is nonuniform and membrane currents are complex. We present the basic ideas that would help informed users make the most efficient use of NEURON.

2,617 citations


"Objective Multi-variable Classifica..." refers methods in this paper

  • ...The framework provides some tools for the construction, simulation, and recording of individual cell-models from biological principle building-blocks as well as networks built from individual neuronal models (see [23] for more details)....

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Journal ArticleDOI
Henry Markram1, Henry Markram2, Eilif Muller2, Srikanth Ramaswamy2, Michael W. Reimann2, Marwan Abdellah2, Carlos Aguado Sanchez2, Anastasia Ailamaki2, Lidia Alonso-Nanclares3, Lidia Alonso-Nanclares4, Nicolas Antille2, Selim Arsever2, Guy Antoine Atenekeng Kahou2, Thomas K. Berger1, Ahmet Bilgili2, Nenad Buncic2, Athanassia Chalimourda2, Giuseppe Chindemi2, Jean Denis Courcol2, Fabien Delalondre2, Vincent Delattre1, Shaul Druckmann5, Shaul Druckmann6, Raphael Dumusc2, James Dynes2, Stefan Eilemann2, Eyal Gal5, Michael Gevaert2, Jean Pierre Ghobril1, Albert Gidon5, Joe W. Graham2, Anirudh Gupta1, Valentin Haenel2, Etay Hay5, Thomas Heinis7, Thomas Heinis2, Juan Hernando4, Michael L. Hines8, Lida Kanari2, Daniel Keller2, John Kenyon2, Georges Khazen2, Yihwa Kim2, James G. King2, Zoltán F. Kisvárday9, Pramod Kumbhar2, Sebastien Lasserre2, Jean Vincent Le Bé1, Bruno R. C. Magalhães2, Angel Merchán-Pérez3, Angel Merchán-Pérez4, Julie Meystre1, Benjamin Roy Morrice2, Jeffrey Muller2, Alberto Muñoz-Céspedes4, Alberto Muñoz-Céspedes3, Shruti Muralidhar1, Keerthan Muthurasa2, Daniel Nachbaur2, Taylor Howard Newton2, Max Nolte2, Aleksandr Ovcharenko2, Juan Palacios2, Luis Pastor10, Rodrigo Perin1, Rajnish Ranjan2, Rajnish Ranjan1, Imad Riachi2, José-Rodrigo Rodríguez4, José-Rodrigo Rodríguez3, Juan Luis Riquelme2, Christian Rössert2, Konstantinos Sfyrakis2, Ying Shi2, Ying Shi1, Julian C. Shillcock2, Gilad Silberberg11, Ricardo Silva2, Farhan Tauheed2, Martin Telefont2, Maria Toledo-Rodriguez12, Thomas Tränkler2, Werner Van Geit2, Jafet Villafranca Diaz2, Richard Walker2, Yun Wang13, Yun Wang14, Stefano M. Zaninetta2, Javier DeFelipe3, Javier DeFelipe4, Sean Hill2, Idan Segev5, Felix Schürmann2 
08 Oct 2015-Cell
TL;DR: A first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat is presented, finding a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms.

1,252 citations