Mathematical modeling of neuronal dynamics during disease

We currently do not understand how neuronal activity leads to cognition. However, we can observe how neuronal activity changes as cognition becomes abnormal. Brain diseases tell us what aspects of neuronal functioning are necessary to maintain brain function and cognition. An essential step to understanding brain function is knowing how to fix it as it becomes dysfunctional. However, studying brain diseases in humans can be challenging because these conditions often span years or decades, making longitudinal studies difficult. Additionally, researchers are restricted to noninvasive measurement methods when studying human subjects. As a result, neuroscience is relying increasingly on quantitative sciences to find patterns in large and complex datasets. Mathematical modeling has become an essential tool to assimilate biological theories and test them in light of experimental data. In this thesis, we study the mathematical modeling of brain diseases. We cover various aspects of modeling, such as developing and analyzing new model formulations, simulating large-scale mathematical models of the human brain, and fitting them to data. First, we integrate mathematical models of Alzheimer’s disease progression and neuronal activity, showing that toxic proteins may cause alterations in brain activity consistent with clinical observations. Second, we develop a model for how neuronal activity affects disease progression, demonstrating the pivotal role neuronal activity plays in shaping disease trajectories. Third, we fit a model for brain-wide neuronal activity to brain cancer patients, discovering significant alterations in brain dynamics. Overall, we develop and analyze mathematical models to study brain diseases and their impact on neuronal activity, demonstrating the benefit of mathematical modeling in studying the mechanisms of brain disease

Mecanismos biofísicos y fuentes de los potenciales extracelulares en el hipocampo

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Aplicada III (Electricidad y Electrónica), leída el 20-11-2015Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEunpu

Light trapping structures for photovoltaics using silicon nanowires and silicon micro-pyramids

The current photovoltaic industry is dominated by crystalline or poly-crystalline Si in a planar pn-junction configuration. The use of silicon nanowire arrays (SiNWA) within this industry has shown great promise due to its application as an anti-reflective layer, as well as benefits in charge carrier extraction. In this work, we use a metal assisted chemical etch process to fabricate SiNWAs onto a dense periodic array of pyramids that are formed using an alkaline etch masked with an oxide layer. The hybrid micro-nano structure acts as an anti-reflective coating with experimental reflectivity below 1% over the visible and near-infrared spectral regions. This represents an improvement of up to 11 and 14 times compared to the pyramid array and SiNWAs on bulk, respectively. In addition to the experimental work, we optically simulate the hybrid structure using the commercial Lumerical FDTD package. The results of the optical simulations support our experimental work, illustrating a reduced reflectivity in the hybrid structure. The nanowire array increases the absorbed carrier density within the pyramid by providing a guided transition of the refractive index along the light path from air into the silicon. Furthermore, electrical simulations which take into account surface and Auger recombination show an effi ciency increase for the hybrid structure of 56% over bulk, 11% over pyramid array and 8.5% over SiNWAs. Opto-electronic modelling was performed by establishing a tool flow to integrate the eff ective optical simulator Lumerical FDTD with the excellent fabrication and electrical simulation capability of Sentaurus TCAD. Interfacing between the two packages is achieved through tool command language and Matlab, off ering fast and accurate electro-optical characteristics of nano-structured PV devices.Open Acces

Improving Associative Memory in a Network of Spiking Neurons

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory

HIGH PERFORMANCE MODELLING AND COMPUTING IN COMPLEX MEDICAL CONDITIONS: REALISTIC CEREBELLUM SIMULATION AND REAL-TIME BRAIN CANCER DETECTION

The personalized medicine is the medicine of the future. This innovation is supported by the ongoing technological development that will be crucial in this field. Several areas in the healthcare research require performant technological systems, which elaborate huge amount of data in real-time. By exploiting the High Performance Computing technologies, scientists want to reach the goal of developing accurate diagnosis and personalized therapies. To reach these goals three main activities have to be investigated: managing a great amount of data acquisition and analysis, designing computational models to simulate the patient clinical status, and developing medical support systems to provide fast decisions during diagnosis or therapies. These three aspects are strongly supported by technological systems that could appear disconnected. However, in this new medicine, they will be in some way connected. As far as the data are concerned, today people are immersed in technology, producing a huge amount of heterogeneous data. Part of these is characterized by a great medical potential that could facilitate the delineation of the patient health condition and could be integrated in our medical record facilitating clinical decisions. To ensure this process technological systems able to organize, analyse and share these information are needed. Furthermore, they should guarantee a fast data usability. In this contest HPC and, in particular, the multicore and manycore processors, will surely have a high importance since they are capable to spread the computational workload on different cores to reduce the elaboration times. These solutions are crucial also in the computational modelling, field where several research groups aim to implement models able to realistically reproduce the human organs behaviour to develop their simulators. They are called digital twins and allow to reproduce the organ activity of a specific patient to study the disease progression or a new therapy. Patient data will be the inputs of these models which will predict her/his condition, avoiding invasive and expensive exams. The technological support that a realistic organ simulator requires is significant from the computational point of view. For this reason, devices as GPUs, FPGAs, multicore devices or even supercomputers are needed. As an example in this field, the development of a cerebellar simulator exploiting HPC will be described in the second chapter of this work. The complexity of the realistic mathematical models used will justify such a technological choice to reach reduced elaboration times. This work is within the Human Brain Project that aims to run a complete realistic simulation of the human brain. Finally, these technologies have a crucial role in the medical support system development. Most of the times during surgeries, it is very important that a support system provides a real-time answer. Moreover, the fact that this answer is the result of the elaboration of a complex mathematical problem, makes HPC system essential also in this field. If environments such as surgeries are considered, it is more plausible that the computation is performed by local desktop systems able to elaborate the data directly acquired during the surgery. The third chapter of this thesis describes the development of a brain cancer detection system, exploiting GPUs. This support system, developed as part of the HELICoiD project, performs a real-time elaboration of the brain hyperspectral images, acquired during surgery, to provide a classification map which highlights the tumor. The neurosurgeon is facilitated in the tissue resection. In this field, the GPU has been crucial to provide a real-time elaboration. Finally, it is possible to assert that in most of the fields of the personalized medicine, HPC will have a crucial role since they consist in the elaboration of a great amount of data in reduced times, aiming to provide specific diagnosis and therapies for the patient

Real-time FGPA implementation of a neuromorphic pitch detection system

This thesis explores the real-time implementation of a biologically inspired pitch detection system in digital electronics. Pitch detection is well understood and has been shown to occur in the initial stages of the auditory brainstem. By building such a system in digital hardware we can prove the feasibility of implementing neuromorphic systems using digital technology. This research not only aims to prove that such an implementation is possible but to investigate ways of achieving efficient and effective designs. We aim to achieve this complexity reduction while maintaining the fine granularity of the signal processing inherent in neural systems. By producing an efficient design we present the possibility of implementing the system within the available resources, thus producing a demonstrable system. This thesis presents a review of computational models of all the components within the pitch detection system. The review also identifies key issues relating to the efficient implementation and development of the pitch detection system. Four investigations are presented to address these issues for optimal neuromorphic designs of neuromorphic systems. The first investigation aims to produce the first-ever digital hardware implementation of the inner hair cell. The second investigation develops simplified models of the auditory nerve and the coincidence cell. The third investigation aims to reduce the most complex stage of the system, the stellate chopper cell array. Finally, we investigate implementing a large portion of the pitch detection system in hardware. The results contained in this thesis enable us to understand the feasibility of implementing such systems in real-time digital hardware. This knowledge may help researchers to make design decisions within the field of digital neuromorphic systems

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Biophysics-based modeling and data analysis of local field potential signal

Understanding the neurophysiological mechanisms of information processing within and across brain regions has always been a fundamental and challenging topic in neuroscience. Considerable works in the brain connectome and transcriptome have laid a profound foundation for understanding brain function by its structure. At the same time, the recent advance in recording techniques allows us to probe the nonstationary brain activity from various spatial and temporal scales. However, how to effectively build the dialogue between the anatomical structure and the dynamical brain signal still needs to be solved. To tackle the problem, we explore interpreting electrophysiology signals with mechanistic models. In chapter 2 we first segregate high-coherent brain signals into different pathways and then connect their dynamics to synaptic properties. Based on a state space model of LFP generation, we explore several preprocessing methods to bias the signal to the synaptic inputs and enhance the separatability of pathway-specific contributions. The separated sources are more reliable with the preprocessing methods, especially in highly coherent states, e.g., awake running. With reliably separated pathways, we further studied their synaptic properties and explored the local directional connections in the hippocampus. The estimated synaptic time constant and pathway connection agrees with well-established anatomical studies. In chapter 3 we explore establishing a simple model to capture the impulse response of passive neurons with detailed dendritic morphology. We validate Green’s function methods based on compartmentalized models by comparing them to numerical simulations and analytical solutions on continuous neuron membrane potentials. A parameterized model based on laminar Green’s function is further developed and helps to infer the anatomical properties, like the input current distribution and cell position, from their spatiotemporal response patterns. The effect of cell position and template are examed. Based on the model of chapter 3, we use the biophysical possible impulse response profile to regularize the source separation in the frequency domain in chapter 4. The components from different frequencies are clustered according to the same latent input distributions. The source separation in better-separated frequency bins from the same pathway helps separation in other highly contaminated frequencies. The optimization is formulated as a probabilistic model to maximize the negentropy as well as spatial likelihood. Similar to dipole approximation for EEG signals, Green’s function method provides an effective approximation to capture biologically possible spatiotemporal patterns and helps to guide the separation. We validated the method on real data with optogenetic stimulation. In chapter 5 we further separate the far-field signals from the local pathway activities according to their physiological properties. We propose a pipeline to reliably separate and automatically detect far-field signal components. Based on this, a toolbox is provided to remove the EMG artifacts and assess the cleaning performance. In the free-running animals, we show that EMG artifacts shadow the high-frequency oscillatory events detection, and EMG cleaning rescues this effect. Overall, this thesis explored multiple possibilities to incorporate neurophysiology knowledge to understand and model the electrical field potential signals.Das Verständnis der neurophysiologischen Mechanismen der Informationsverarbeitung innerhalb und zwischen Gehirnregionen war schon immer ein grundlegendes und herausforderndes Thema in den Neurowissenschaften. Weitreichende Arbeiten zum Konnektom und Transkriptom des Gehirns haben eine Grundlage für das Verständnis der Gehirnfunktion gelegt. Des Weiteren ermöglicht uns der derzeitige Fortschritt in der Aufnahmetechnik, die nicht stationäre Gehirnaktivität auf verschiedenen räumlichen und zeitlichen Skalen zu untersuchen. Wie jedoch die anatomischen Strukturen und die dynamischen Gehirnsignal effektiv zusammen wirken können, muss jedoch noch gelöst werden. Um dieses Problem anzugehen, untersuchen wir die Interpretation elektrophysiologischer Signale mit mechanistischen Modellen. In Kapitel 2 trennen wir zunächst die hochkohärenten Gehirnsignale in verschiedene Leitungsbahnen und verbinden dann die Dynamik mit synaptischen Eigenschaften. Basierend auf einem Zustandsraummodell zur Erzeugung lokaler Feldpotentiale (LFP) untersuchen wir verschiedene Vorverarbeitungsmethoden, die die Signale bestmöglich auf die synaptischen Eingangsströme ausrichten und die Trennbarkeit von leitungsbahnspezifischen Beiträgen verbessert. Die Trennung der Signalquellen ist durch das Vorverarbeitungsverfahren insbesondere während hochkohärenter Verhaltenszustände (z. B. laufen im Wachzustand) zuverlässiger. Mit zuverlässig getrennten Leitungsbahnen konnten wir die entsprechenden synaptischen Eigenschaften weiter untersuchen und die lokalen gerichteten Verbindungen im Hippocampus untersuchen. Die geschätzte synaptische Zeitkonstante und die Verbindungen der Leitungsbahnen stimmen mit etablierten anatomischen Studien überein. In Kapitel 3 untersuchen wir die Erstellung eines einfachen Modells zur Beschreibung der Impulsantwort passiver Neuronen mit detaillierter dendritischer Morphologie. Wir validieren Greensche Funktionsmethoden basierend auf kompartimentierten Modellen, indem wir sie mit numerischen Simulationen und analytischen Lösungen des kontinuierlichen Membranpotentials von Neuronen vergleichen. Ein parametrisiertes Modell, das auf der laminaren Greenschen Funktion basiert, wird weiterentwickelt. Es hilft dabei, die anatomischen Eigenschaften - die Verteilung des Eingangsstroms und die Zellposition - aus ihren raumzeitlichen Reaktionsmustern abzuleiten. Die Auswirkung der Zellposition und des Templates werden untersucht. Basierend auf dem Modell aus Kapitel 3 verwenden wir in Kapitel 4 das biophysikalisch mögliche Profil der Impulsantwort, um die Quellentrennung im Frequenzbereich festzulegen. Die Komponenten verschiedener Frequenzen werden nach derselben latenten Eingangsverteilungen geclustert. Die Quellentrennung in besser getrennten Frequenzbereichen derselben Leitungsbahn hilft bei der Quelltrennung in anderen stark kontaminierten Frequenzbereichen. Die Optimierung wird als probabilistisches Modell formuliert, um sowohl die Negentropie als auch die räumliche Wahrscheinlichkeit zu maximieren. Ähnlich wie die Dipolnäherungen für EEG-Signale bietet die Greensche Funktionsmethode eine effektive Annäherung, um biologisch mögliche raumzeitliche Muster zu erfassen, und hilft, die Quellen zu trennen. Wir haben die Methode an realen Daten mit optogenetischer Stimulation validiert. Im Kapitel 5 trennen wir weiter die Fernfeldsignale von den Signalen der lokalen Leitungsbahnen nach ihren physiologischen Eigenschaften. Wir schlagen eine Methode vor, die es erlaubt, Fernfeld-Signalkomponenten zuverlässig von lokaler Aktivitaet zu trennen und automatisch zu erkennen. Es wird eine Toolbox bereitgestellt, die EMG-Artefakte entfernt und die bereinigten Signale bewertet. In Ableitungen von freilaufenden Tieren zeigen wir, dass EMG-Artefakte die Erkennung von hochfrequenten Oszillationen beeintraechtigt, aber nach der Bereinigung des EMG-Signals erkannt werden kann. Insgesamt untersucht diese Dissertation mehrere Möglichkeiten die elektrischen Feldpotentiale neuronaler Aktivität unter Einbeziehung neurophysiologischen Wissens zu modellieren und zu verstehen

Encoding and processing of sensory information in neuronal spike trains

Recently, a statistical signal-processing technique has allowed the information carried by single spike trains of sensory neurons on time-varying stimuli to be characterized quantitatively in a variety of preparations. In weakly electric fish, its application to first-order sensory neurons encoding electric field amplitude (P-receptor afferents) showed that they convey accurate information on temporal modulations in a behaviorally relevant frequency range (<80 Hz). At the next stage of the electrosensory pathway (the electrosensory lateral line lobe, ELL), the information sampled by first-order neurons is used to extract upstrokes and downstrokes in the amplitude modulation waveform. By using signal-detection techniques, we determined that these temporal features are explicitly represented by short spike bursts of second-order neurons (ELL pyramidal cells). Our results suggest that the biophysical mechanism underlying this computation is of dendritic origin. We also investigated the accuracy with which upstrokes and downstrokes are encoded across two of the three somatotopic body maps of the ELL (centromedial and lateral). Pyramidal cells of the centromedial map, in particular I-cells, encode up- and downstrokes more reliably than those of the lateral map. This result correlates well with the significance of these temporal features for a particular behavior (the jamming avoidance response) as assessed by lesion experiments of the centromedial map