search query: @keyword neocortex / total: 2
reference: 2 / 2
« previous | next »
Author:Laakso, Risto Sakari
Title:Algorithmic description of pyramidal neuron populations from mouse neocortex
Algoritminen kuvaus hiiren etuaivojen pyramiidisista neuronipopulaatioista
Publication type:Master's thesis
Publication year:2010
Pages:[6] + 40 s. + liitt. 21      Language:   eng
Department/School:Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
Main subject:Laskennallinen tekniikka   (S-114)
Supervisor:Lampinen, Jouko
Instructor:Krieger, Patrik
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201203151586
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions

Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

Logging on to the customer computers

  • Aalto University staff members log on to the customer computer using the Aalto username and password.
  • Other customers log on using a shared username and password.

Opening a thesis

  • On the desktop of the customer computers, you will find an icon titled:

    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

  • You can either print the thesis or read it on the customer computer screen.
  • You cannot save the thesis file on a flash drive or email it.
  • You cannot copy text or images from the file.
  • You cannot edit the file.

Printing the thesis

  • You can print the thesis for your personal study or research use.
  • Aalto University students and staff members may print black-and-white prints on the PrintingPoint devices when using the computer with personal Aalto username and password. Color printing is possible using the printer u90203-psc3, which is located near the customer service. Color printing is subject to a charge to Aalto University students and staff members.
  • Other customers can use the printer u90203-psc3. All printing is subject to a charge to non-University members.
Location:P1 Ark Aalto  932   | Archive
Keywords:computational neuroscience
neuroanatomy
dendrite morphology
neocortex
pyramidal cell
Netmorph
stochastic optimization
Retrospective Approximation
Nelder-Mead
Kolmogorov-Smirnov
laskennallinen neurotiede
neuroanatomia
dendriittien morfologia
neokorteksi
pyramidineuroni
Netmorph
stokastinen optimointi
Retrospective Approximation
Nelder-Mead
Kolmogorov-Smirnov
Abstract (eng): Large pyramidal neurons from the mouse neocortex have been studied by Groh et al (2010).
In this thesis the neuronal data was statistically analyzed for structural features, and used to optimize computational models to create realistic neuronal morphologies.
The mouse data and model generated morphologies were studied for populational differences in cortical areas.

Two populations of layer 5 pyramidal neurons were genetically labeled with enhanced green fluorescent protein and extracted from the somatosensory (barrel) cortex and the visual cortex in a previous study by Groh et al.
The neuronal morphologies were reconstructed in 3D using microscope and Neurolucida software.
The 3D data was analyzed in this thesis for the branching and length features needed for model optimization.

Computational models were constructed in the Netmorph simulator to generate large pyramidal neurons.
The model parameters were optimized with the Retrospective Approximation method with a Nelder-Mead direct search to generate statistical distributions similar to the mouse data.
The model optimization was evaluated with Kolmogorov-Smirnov goodness-of-fit tests.

The optimized Netmorph models were able to generate realistic and statistically similar morphologies to the experimental data.
Large-scale networks could also be constructed and used for subsequent electrophysiological simulation in e.g.
NEURON software.
Abstract (fin): Groh et al (2010) tutkivat suuria pyramiidineuroneja hiiren neokorteksissa.
Tässä työssä kyseistä neuronidataa analysoitiin statististen menetelmien avulla ja käytettiin laskennallisen neuronikasvumallin optimointiin.
Hiiren aivoista saadun datan ja laskennallisella mallilla saatavien neuronirakenteiden eroja tutkittiin sekä vertailtiin aivojen eri osissa olevia eroja.

Kaksi L5 pyramidineuronipopulaatiota merkittiin geneettisesti (etv, glt) vihreällä fluoresenssiproteiinilla.
Populaatiot poimittiin somatosensoorisesta (barrel) korteksista ja visuaalikorteksista.
Neuronien rakenteet rekonstruoitiin kolmiulotteisena mikroskoopin ja Neurolucida-ohjelmiston avulla (Groh et al 2010).
Kolmiulotteinen data analysoitiin tässä työssä haarautumis- ja pituusominaisuuksien saamiseksi mallin optimisointia varten.

Netmorph-simulaattorilla (Koene et al 2009) voidaan tehdä malleja suurien pyramidineuronien luomiseksi.
Mallien parametreja voidaan optimoida stokastisilla menetelmillä.
Menetelmäksi valittiin Retrospecive Approximation-optimointi jossa käytettiin lisäksi Nelder-Mead suorahakua.
Optimoinnissa malli pyritään saamaan vastaamaan hiiren aivoista saatua dataa.
Optimoinnin onnistumisen arviointiin käytettiin Kolmogorov-Smirnov testiä.

Optimoiduilla Netmorph-malleilla saatiin luotua realistia neuronirakenteita jotka vastasivat myös statistisesti tietyin osin hiirestä saatuja näytteitä.
Suurien neuroniverkkojen luominen olisi mahdollista saatujen tulosten avulla, ja niiden elektrofysiologisia ominaisuuksia voitaisiin tutkia esimerkiksi NEURON-ohjelmistolla simuloiden.
ED:2011-05-06
INSSI record number: 41647
+ add basket
« previous | next »
INSSI