Simulation of sensory-evoked signal flow in anatomically realistic models of neural networks

In this thesis, a new concept for development and simulation of anatomically and functionally constrained models of signal flow in neural networks is described. This approach consists of the following tools: 1. A standardized anatomical reference frame of the brain region studied and registration methods to integrate anatomical data from different experiments with the highest precision possible. 2. A method for determining morphological neuron types to allow correlation between measurements of the morphology and functional responses of individual neurons. 3. A tool to build an average three-dimensional (3D) statistical model of the neural networks in a brain region based on a representative sparse sample of all neuron types present in the brain region. This model contains 3D morphological models for every neuron in the brain region, as well as the total number and 3D distribution of synaptic contacts between them. 4. A method to activate the network based on measured responses of different neuron types, and to simulate the response of individual neurons representative of different cell types within this network model. The feasibility and validity of this process is demonstrated on the example of rat vibrissal cortex. The 3D model of this primary sensory area in cortex contains ∼ 530,000 neurons of 16 different types and ∼ 6 × 10^9 thalamocortical and intracortical synapses. Activation of this model with functional responses measured after whisker touch and simulation of the responses of different neuron types shows that the simulated model responses match experimental measurements. This allowed investigating how robust sensory-evoked responses after different sensory stimuli are formed in different neuron types using computer simulations, and to make predictions to experimentally test these hypotheses.Diese Dissertation beschreibt einen neuartigen Ansatz zur Entwicklung und Simulation von Modellen des Signalflusses in neuronalen Netzwerken unter anatomisch und funktionell realistischen Randbedingungen. Dieser Ansatz besteht aus den folgenden Methoden: 1. Ein standardisiertes anatomisches Referenzsystem der betrachteten Hirnregion und Registrierungsmethoden die es erlauben anatomische Daten aus unterschiedlichen Experimenten mit höchstmöglicher Genauigkeit zu integrieren. 2. Eine Methode zur Bestimmung morphologischer Typen von Nervenzellen um Messungen von der Morphologie und funktioneller Antworten einzelner Nervenzellen in Bezug zu setzen. 3. Eine Methode um ein mittleres dreidimensionales (3D) statistisches Modell der neuronalen Netzwerke in einer Hirnregion zu bauen, das auf einer repräsentativen Stichprobe aller Nervenzelltypen in dieser Hirnregion beruht. Dieses Modell beinhaltet 3D morphologische Modelle für jede Nervenzelle in der Hirnregion, und die Zahl und 3D Verteilung synaptischer Verknüpfungen zwischen diesen. 4. Eine Methode um dieses Netzwerk aufgrund von gemessenen Antworten unterschiedlicher Nervenzelltypen zu aktivieren, und die Antwort einzelner repräsentativer Nervenzellen bestimmten Typs innerhalb dieses Netzwerkmodells zu simulieren. Die Machbarkeit und Gültigkeit dieses Ansatzes wird am Beispiel des Tasthaarsystems im Kortex der Ratte demonstriert. Das 3D Modell dieses primären sensorischen Kortex enthält ∼ 530000 Nervenzellen von 16 unterschiedlichen Typen und ∼ 6 × 10^9 thalamokortikale und intrakortikale Synapsen. Aktivierung dieses Modells mit gemessen funktionellen Antworten auf passive Berührung eines Schnurrhaares und Simulation der Antworten unterschiedlicher Nervenzelltypen zeigt dass die simulierten Antworten mit experimentellen Messungen übereinstimmen. Dies erlaubt es mit Hilfe von Computersimulationen zu untersuchen wie robuste Antworten auf unterschiedliche Sinnesreize in unterschiedlichen Nervenzelltypen entstehen, und experimentell überprüfbare Vorhersagen zu machen

From single cells and single columns to cortical networks: dendritic excitability, coincidence detection and synaptic transmission in brain slices and brains

Although patch pipettes were initially designed to record extracellularly the elementary current events from muscle and neuron membranes, the whole-cell and loose cell-attached recording configurations proved to be useful tools for examination of signalling within and between nerve cells. In this Paton Prize Lecture, I will initially summarize work on electrical signalling within single neurons, describing communication between the dendritic compartments, soma and nerve terminals via forward- and backward-propagating action potentials. The newly discovered dendritic excitability endows neurons with the capacity for coincidence detection of spatially separated subthreshold inputs. When these are occurring during a time window of tens of milliseconds, this information is broadcast to other cells by the initiation of bursts of action potentials (AP bursts). The occurrence of AP bursts critically impacts signalling between neurons that are controlled by target-cell-specific transmitter release mechanisms at downstream synapses even in different terminals of the same neuron. This can, in turn, induce mechanisms that underly synaptic plasticity when AP bursts occur within a short time window, both presynaptically in terminals and postsynaptically in dendrites. A fundamental question that arises from these findings is: what are the possible functions of active dendritic excitability with respect to network dynamics in the intact cortex of behaving animals?' To answer this question, I highlight in this review the functional and anatomical architectures of an average cortical column in the vibrissal (whisker) field of the somatosensory cortex (vS1), with an emphasis on the functions of layer 5 thick-tufted cells (L5tt) embedded in this structure. Sensory-evoked synaptic and action potential responses of these major cortical output neurons are compared with responses in the afferent pathway, viz. the neurons in primary somatosensory thalamus and in one of their efferent targets, the secondary somatosensory thalamus. Coincidence-detection mechanisms appear to be implemented in vivo as judged from the occurrence of AP bursts. Three-dimensional reconstructions of anatomical projections suggest that inputs of several combinations of thalamocortical projections and intra- and transcolumnar connections, specifically those from infragranular layers, could trigger active dendritic mechanisms that generate AP bursts. Finally, recordings from target cells of a column reveal the importance of AP bursts for signal transfer to these cells. The observations lead to the hypothesis that in vS1 cortex, the sensory afferent sensory code is transformed, at least in part, from a rate to an interval (burst) code that broadcasts the occurrence of whisker touch to different targets of L5tt cells. In addition, the occurrence of pre- and postsynaptic AP bursts may, in the long run, alter touch representation in cortex

Imaging fast neural activity in the brain with Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an emerging medical imaging technique that can be employed to reconstruct the internal conductivity of an object from measurements made on the boundary. One proposed application for EIT is in head imaging, including imaging of impedance changes that occur with neuronal depolarisation and the imaging of acute stroke. The work of this thesis was aimed at advancing the imaging of brain pathology and function, with particular focus on the imaging of fast neural activity. Chapter 1 is a review of other brain imaging techniques, the principles of bioimpedance and EIT, and of previous impedance recordings of fast neural activity. Chapter 2 was a comparison of reconstruction algorithms for the detection of acute stroke using EIT in a realistic head-shaped tank, which entailed assessing boundary voltage rejection methods and quantitative analysis of image quality to determine the best reconstruction algorithms for the detection of acute stroke. In chapter 3, an EIT imaging dataset of fast neural activity, previously collected in a rat model, was assessed using second-level statistical parametric mapping (SPM) and the spatio-temporal propagation of the activity assessed and compared to the neurophysiological literature, which was reviewed in chapter 1. The analysis undertaken in chapter 3 illustrated some key methodological issues, which were addressed in chapter 4: new high resolution meshes and better optimised matrix inversion were employed, a new algorithm for electrode alignment was developed, also the use of SPM was validated by applying it to control datasets and through the use of statistical non-parametric mapping. Chapters 5 and 6 detail work attempting to cross-validate the use of EIT to image fast neural activity by employing a physiological stimulus, mechanical whisker displacement, and comparing the findings to other neurophysiological techniques recorded in the same model. Chapter 5 details work to validate the model and the impedance findings in this model as compared to previously published neurophysiological results, while chapter 6 details the use of other neurophysiological techniques for cross-validation

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New tools and specification languages for biophysically detailed neuronal network modelling

Increasingly detailed data are being gathered on the molecular, electrical and anatomical properties of neuronal systems both in vitro and in vivo. These range from the kinetic properties and distribution of ion channels, synaptic plasticity mechanisms, electrical activity in neurons, and detailed anatomical connectivity within neuronal microcircuits from connectomics data. Publications describing these experimental results often set them in the context of higher level network behaviour. Biophysically detailed computational modelling provides a framework for consolidating these data, for quantifying the assumptions about underlying biological mechanisms, and for ensuring consistency in the explanation of the phenomena across scales. Such multiscale biophysically detailed models are not currently in wide- spread use by the experimental neuroscience community however. Reasons for this include the relative inaccessibility of software for creating these models, the range of specialised scripting languages used by the available simulators, and the difficulty in creating and managing large scale network simulations. This thesis describes new solutions to facilitate the creation, simulation, analysis and reuse of biophysically detailed neuronal models. The graphical application neuroConstruct allows detailed cell and network models to be built in 3D, and run on multiple simulation platforms without detailed programming knowledge. NeuroML is a simulator independent language for describing models containing detailed neuronal morphologies, ion channels, synapses, and 3D network connectivity. New solutions have also been developed for creating and analysing network models at much closer to biological scale on high performance computing platforms. A number of detailed neocortical, cerebellar and hippocampal models have been converted for use with these tools. The tools and models I have developed have already started to be used for original scientific research. It is hoped that this work will lead to a more solid foundation for creating, validating, simulating and sharing ever more realistic models of neurons and networks

High Precision Anatomy for MEG

Magnetoencephalography (MEG) is a non-invasive brain imaging method with high temporal resolution but relatively poor spatial resolution as compared to some other non-invasive techniques. This thesis examines how the spatial resolution of MEG can be improved using new recording paradigms in which the subject’s head position is fixed and known in advance. This is accomplished by using subject-specific head casts made using a combination of structural MRI and 3D printing technology. The resulting high-precision spatial models allow one to make inference at spatial scales of the order of cortical laminae. This thesis outlines the design of the head casts and examines the potential theoretical and empirical advances they offer. Specifically I outline simulation and then empirical investigations showing it is possible to non-invasively distinguish between electrophysiological signals in different layers of the cortex