000168660 001__ 168660
000168660 005__ 20190509132411.0
000168660 0247_ $$2doi$$a10.5075/epfl-thesis-5208
000168660 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis5208-2
000168660 02471 $$2nebis$$a6603540
000168660 037__ $$aTHESIS
000168660 041__ $$aeng
000168660 088__ $$a5208
000168660 245__ $$aEmergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column
000168660 269__ $$a2011
000168660 260__ $$bEPFL$$c2011$$aLausanne
000168660 300__ $$a262
000168660 336__ $$aTheses
000168660 520__ $$aThe cerebral cortex occupies nearly 80% of the entire  volume of the mammalian brain and is thought to subserve  higher cognitive functions like memory, attention and sensory  perception. The neocortex is the newest part in the evolution  of the cerebral cortex and is perhaps the most intricate  brain region ever studied. The neocortical microcircuit is  the smallest Œecosystem‚ of the neocortex that  consists of a rich assortment of neurons, which are diverse  in both their morphological and electrical properties. In the  neocortical microcircuit, neurons are horizontally arranged  in 6 distinct sheets called layers. The fundamental operating  unit of the neocortical microcircuit is believed to be the  Neocortical Column (NCC). Functionally, a single NCC is an  arrangement of thousands of neurons in a vertical fashion  spanning across all the 6 layers. The structure of the entire  neocortex arises from a repeated and stereotypical  arrangement of several thousands of such columns, where  neurons transmit information to each other through  specialized points of information transfer called synapses.  The dynamics of synaptic transmission can be as diverse as  the neurons defining a connection and are crucial to foster  the functional properties of the neocortical  microcircuit. The Blue Brain Project (BBP) is the first comprehensive  endeavour to build a unifying model of the NCC by systematic  data integration and biologically detailed simulations.  Through the past 5 years, the BBP has built a facility for a  data-constraint driven approach towards modelling and  integrating biological information across multiple levels of  complexity. Guided by fundamental principles derived from  biological experiments, the BBP simulation toolchain has  undergone a process of continuous refinement to facilitate  the frequent construction of detailed in silico models  of the NCC. The focus of this thesis lies in characterizing the  functional properties of in silico synaptic  transmission by incorporating principles of synaptic  communication derived through biological experiments. In  order to study in silico synaptic transmission it is  crucial to gain an understanding of the key players  influencing the manner in which synaptic signals are  processed in the neocortical microcircuit - ion channel  kinetics and distribution profiles, single neuron models and  dynamics of synaptic pathways. First, by means of exhaustive literature survey, I  identified ion channel kinetics and their distribution  profiles on neocortical neurons to build in silico ion  channel models. Thereafter, I developed a prototype framework  to analyze the somatic and dendritic features of single  neuron models constrained by ion channel kinetics. Finally,  within a simulation framework integrating the ion channels,  single neuron models and dynamics of synaptic transmission, I  replicated in vitro experimental protocols in  silico, to characterize the transmission properties of  monosynaptic connections. These synaptic connections, arising  from the axo-dendritric apposition of neuronal arbours were  sampled across many instances of in silico NCC models  constructed a priori through the BBP simulation  toolchain. In this thesis, I show that when principles of synaptic  transmission derived from in vitro experiments are  incorporated to model in silico synaptic connections,  the resulting anatomy and physiology of synaptic connections  modelled from elementary biological rules closely match in  vitro data. This thesis work demonstrates that the  average synaptic response properties in silico are  robust to perturbations in the anatomical and physiological  properties of modelled connections in the local neocortical  microcircuit. A fundamental discovery through this thesis is  an insight into the function of the local neocortical  microcircuit by examining the effect of morphological  diversity on in silico synaptic transmission. I  demonstrate here that intrinsic morphological diversity  confers an invariance to the average synaptic response  properties in silico in the local neocortical  microcircuit, termed "microcircuit level robustness and  invariance".
000168660 6531_ $$aneocortical column
000168660 6531_ $$ain silico
000168660 6531_ $$ain vitro
000168660 6531_ $$acalibration
000168660 6531_ $$avalidation
000168660 6531_ $$aion channel models
000168660 6531_ $$asingle neuron models
000168660 6531_ $$asynaptic transmission
000168660 6531_ $$aprobabilistic synapse model
000168660 6531_ $$asynaptic pathways
000168660 6531_ $$aexcitatory and inhibitory connections
000168660 6531_ $$acolonne néocorticale
000168660 6531_ $$ain silico
000168660 6531_ $$ain vitro
000168660 6531_ $$acalibration
000168660 6531_ $$avalidation
000168660 6531_ $$amodèles de canaux ioniques
000168660 6531_ $$amodèles de neurones individuels
000168660 6531_ $$amodèle synaptique probabiliste
000168660 6531_ $$avoies synaptiques
000168660 6531_ $$aconnexions excitatrices et inhibitrices
000168660 700__ $$aRamaswamy, Srikanth
000168660 720_2 $$aMarkram, Henry$$edir.$$g150822$$0240392
000168660 720_2 $$aHill, Sean Lewis$$edir.
000168660 8564_ $$uhttps://infoscience.epfl.ch/record/168660/files/EPFL_TH5208.pdf$$zTexte intégral / Full text$$s12043981$$yTexte intégral / Full text
000168660 909C0 $$xU10458$$0252120$$pLNMC
000168660 909C0 $$xU12910$$0252531$$pGR-HILL
000168660 909CO $$ooai:infoscience.tind.io:168660$$qDOI2$$qGLOBAL_SET$$pSV$$pthesis$$pthesis-bn2018$$pDOI
000168660 917Z8 $$x182396
000168660 918__ $$dEDNE$$aSV
000168660 919__ $$aLNMC
000168660 920__ $$b2011
000168660 970__ $$a5208/THESES
000168660 973__ $$sPUBLISHED$$aEPFL
000168660 980__ $$aTHESIS