Forecasting Natural Events Using Axonal Delay

The ability to forecast natural phenomena relies on understanding causality. By definition this understanding must include a temporal component. In this paper, we consider the ability of an emerging class of neural network, which encode temporal information into the network, to perform the difficult task of Natural Event Forecasting. The Axonal Delay Network (ADN) models axonal delay in order to make predictions about sunspot activity, the Auroral Electrojet (AE) index and daily temperatures during a heatwave. The performance of this network is benchmarked against older types of neural networks; including the Multi-Layer Perceptron (MLP) network and Functional Link Neural Network (FLNN). The results indicate that the inherent temporal characteristics of the Axonal Delay Network make it well suited to the processing and prediction of natural phenomena

Inspired by nature: timescale-free and grid-free event-based computing with\ua0spiking neural networks

Computer vision is enjoying huge success in visual processing applications such as facial recognition, object identification, and navigation. Most of these studies work with traditional cameras which produce frames at predetermined fixed time intervals. Real life visual stimuli are, however, generated when changes occur in the environment and are irregular in timing. Biological visual neural systems operate on these changes and are hence free from any fixed timescales that are related to the timing of events in visual input.Inspired by biological systems, neuromorphic devices provide a new way to record visual\ua0data. These devices typically have parallel arrays of sensors which operate asynchronously. They have particular potential for robotics due to their low latency, efficient use of bandwidth and low power requirements. There are a variety of neuromorphic devices for detecting different sensory information; this thesis focuses on using the Dynamic Vision Sensor (DVS) for visual data collection.Event-based sensory inputs are generated on demand as changes happen in the environment. There are no systematic timescales in these activities and the asynchronous nature of the sensors adds to the irregularity of time intervals between events, making event-based data timescale-free. Although the array of sensors are arranged as a grid in vision sensors generally, events in the real world exist in continuous space. Biological systems are not restricted to grid-based sampling, and it is an open question whether event-based data could similarly take advantage of grid-free processing algorithms. To study visual data in a way which is timescale-free and grid-free, which is\ua0 fundamentally different from traditional video data sampled at fixed time intervals which are dense and rigid in space, requires conceptual viewpoints and methods of computation which are not typically employed in existing studies.Bio-inspired computing involves computational components that mimic or at least take inspiration from how nature works. This fusion of engineering and biology often provides insights into complex computational problems. Artificial neural networks, a computing paradigm that is inspired by how our brains work, have been studied widely with visual data. This thesis uses a type of artificial neural network—event-based spiking neural networks—as the basic framework to process event-based visual data.Building upon spiking neural networks, this thesis introduces two methods that process event-based data with the principles of being timescale-free and grid-free. The first method preprocesses events as distributions of Gaussian shaped spatiotemporal volumes, and then introduces a new neuron model with time-delayed dendrites and dendritic and axonal computation as the main building blocks of the spiking neural network to perform long-term predictions. Gaussians are used for simplicity purposes. This Gaussian-based method is shown in this thesis to outperform a commonly used iterative prediction paradigm on DVS data.The second method involves a new concept for processing event-based data based on the “light cone” idea in physics. Starting from a given point in real space at a given time, a light cone is the set of points in spacetime reachable without exceeding the speed of light, and these points trace out spacetime trajectories called world lines. The light cone concept is applied to DVS data. As an object moves with respect to the DVS, the events generated are related by their speeds relative to the DVS. An observer can calculate possible world lines for each point but has no access to the correct one. The idea of a “motion cone” is introduced to refer to the distribution of possible world lines for an event. Motion cones provide a novel theory for the early stages of visual processing. Instead of spatial clustering, world lines produce a new representation determined by a speed-based clustering of events. A novel spiking neural network model with dendritic connections based on motion cones is proposed, with the ability predict future motion pattern in a long-term prediction.Freedom from timescales and fixed grid sizes are fundamental characteristics of neuromorphic event-based data but few algorithms to date exploit their potential. Focusing on the inter-event relationship in the continuous spatiotemporal volume can preserve these features during processing. This thesis presents two examples of incorporating the features of being timescale-free and grid-free into algorithm development and examines their performance on real world DVS data. These new concepts and models contribute to the neuromorphic computation field by providing new ways of thinking about event-based representations and their associated algorithms. They also have the potential to stimulate rethinking of representations in the early stages of an event-based vision system. To aid algorithm development, a benchmarking data set containing data ranging from simple environment changes collected from a stationary camera to complex environmentally rich navigation performed by mobile robots has been collated. Studies conducted in this thesis use examples from this benchmarking data set which is also made available to the public

When to Initiate Disease-Modifying Drugs for Relapsing Remitting Multiple Sclerosis in Adults?

For patients with Relapsing Remitting Multiple Scierosis Beta Interfaerons and Glatiramer Acetate were the first to be licensed for treatment. This review deals with one major question: when to initiate therapy? Through exploring the unique characteristics of the disease and treatement we suggest an approach that should be helpful in the process of decision-making

Impact of arterial stiffness on white matter microstructure in the elderly

La rigidité artérielle fait référence à la perte d'élasticité principalement dans les grandes artères telles que l'aorte et les carotides. On sait que la rigidité artérielle chroniquement élevée contribue à des modifications vasculaires cérébrales telles que des lésions parenchymateuses de la substance blanche cérébrale via une modification du flux sanguin cérébral. En particulier, parmi les structures perfusées par les artérioles fournies par les artères cérébrales antérieure et moyenne, le corps calleux, la capsule interne, la corona radiata et le faisceau longitudinal supérieur sont les plus vulnérables à l’hypoperfusion. Des études antérieures ont montré que l'augmentation de la rigidité artérielle évaluée par la vitesse de l'onde de pouls carotide-fémorale (cfPWV) est associée à une diminution de l'anisotropie fractionnelle (FA) et à une augmentation de la diffusivité radiale (RD). On a émis l'hypothèse que les altérations au niveau des régions vulnérables de la substance blanche (par exemple, le corps calleux, la capsule interne) seraient probablement liées à la démyélinisation axonale. Cependant, bien que la RD a auparavant été corrélée avec la démyélinisation axonale, l'imagerie de diffusion est principalement aveugle à la myéline. En revanche, l'imagerie par transfert de magnétisation (MT) est une métrique adaptée pour estimer la fraction volumique de myéline. De plus, malgré leur sensibilité à l'organisation des fibres axonales, les métriques de tenseur de diffusion (DTI) telles que les FA et RD manquent de spécificité pour la microstructure tissulaire individuelle. Des modèles microstructuraux plus avancés tels que l’imagerie dispersion et de l'orientation des neurites (NODDI) fournissent des outils pour disséquer les changements microstructuraux derrière les mesures DTI. Dans l'article 1, nous avons utilisé les métriques de DTI et basé sur le MT pour examiner de plus près l'interaction entre la rigidité artérielle et la microstructure de la substance blanche chez les personnes âgées de plus de 65 ans. Nous avons constaté que la mesure de référence absolue de la rigidité artérielle, la mesure de la vitesse de l'onde de pouls entre l’artère fémorale et carotidienne (cfPWV) était associée à l'organisation axonale des fibres telle que reflétée par FA et RD plutôt qu'à la démyélinisation dans les régions de la substance blanche qui ont été précédemment désignées comme vulnérables à rigidité artérielle. Dans notre deuxième article, nous avons utilisé le modèle NODDI pour approfondir la relation entre le cfPWV et l'organisation axonale. Nos résultats ont montré que la cfPWV est positivement associée à la diffusion extracellulaire de l'eau (ISOVF), ce qui signifie que la rigidité artérielle peut entraîner une dispersion axonale, diminuant la contrainte de directionnalité de l'eau le long des axones. En outre, nous avons constaté que la rigidité artérielle est associée à une augmentation de la densité des fibres dans le corps calleux tel que mesuré par l’ICVF, ce qui pourrait suggérer que les personnes à risque plus élevé de déclin cognitif présentent des mécanismes compensatoires précoces avant l'apparition de signes cliniques de déclin cognitif. Compte tenu de la forte interaction entre la rigidité artérielle et le déclin à la fois de la structure du cerveau et des fonctions cérébrales, on peut envisager un avenir meilleur où la rigidité artérielle sera mesurée dans la pratique clinique de routine afin d'identifier les personnes à risque plus élevé d’altérations de la substance blanche et de déclin cognitif. Ces personnes pourraient bénéficier de programmes multi-interventionnels visant à préserver la structure et la fonction cérébrale. Un seuil de rigidité artérielle est donc nécessaire pour identifier ces individus. L'article 3 présente la première estimation d'une valeur seuil de cfPWV à laquelle la rigidité artérielle affecte la microstructure de la substance blanche chez les personnes âgées. Nos résultats suggèrent que le seuil actuel de 10 m / s de cfPWV adopté par la Société européenne d'hypertension n'est peut-être pas le seuil optimal pour diviser les individus en groupes à risque neurovasculaire élevé et faible. Au lieu de cela, nos résultats suggèrent que le seuil de cfPWV est plus susceptible d’être autour de 8,5 m / s. Bien que le cfPWV offre une excellente valeur pronostique chez les adultes, il reste malheureusement principalement utilisé dans la recherche en raison du besoin d'experts formés pour cette mesure. À l'inverse, la mesure de l'indice de rigidité artérielle (ASI) à l'aide de la pléthysmographie suscite un intérêt croissant ces dernières années en raison de son approche simple à utiliser. Dans l'article 4, nous avons étudié la relation entre l'ASI et la pression pulsée (PP) qui est une mesure indirecte de la rigidité artérielle, avec la FA et les lésions de la substance blanche chez les participants du UK Biobank. Nous avons constaté que la PP prédit mieux l'intégrité de la substance blanche que l'ASI chez les participants de moins de 75 ans. Cette constatation implique que l'ASI de la pléthysmographie ne semble pas être une mesure fiable de la rigidité artérielle chez les personnes âgées. Des études futures sont évidemment nécessaires pour valider nos résultats, en particulier notre seuil de cfPWV. Une fois ce seuil validé, nous envisageons un avenir radieux où la mesure du cfPWV sera non seulement utilisée pour aider à sélectionner les personnes qui bénéficieraient le plus d'un programme multi-interventionnel visant à préserver l'intégrité cérébrale, mais pourrait également être utilisée pour surveiller l’effet d’une telle intervention.Arterial stiffness refers to the loss of elasticity mainly in large arteries such as the aorta and carotids. Chronically elevated arterial stiffness contributes to cerebrovascular changes such as cerebral white matter parenchymal damage via an alteration of cerebral blood flow. In particular, among the areas perfused by arterioles supplied by the anterior and middle cerebral arteries, the corpus callosum, the internal capsule, the corona radiata, and the superior longitudinal fasciculus are more vulnerable to cerebral hypoperfusion. Previous studies have shown that increased arterial stiffness as assessed by carotid-femoral pulse wave velocity (cfPWV) is associated with a decrease in fractional anisotropy (FA) and increase in radial diffusivity (RD). It was hypothesized that alterations in vulnerable white matter tracts (e.g. corpus callosum, internal capsule) are likely to be related to axonal demyelination. However, while RD was previously correlated with axonal demyelination, diffusion imaging is mostly blind to myelin. In contrast magnetization transfer (MT) imaging is a tailored metric to estimate myelin volume fraction. Moreover, despite their sensitivity to axon fiber organization, diffusion tensor metrics (DTI) such as FA and RD lack specificity for individual tissue microstructure. More advanced microstructural model such as neurite orientation dispersion and density imaging (NODDI) give tools to disecate the microstructural changes behind DTI metrics. In Article 1 we used DTI and MT based metric to look more closely at the interplay between arterial stiffness and white matter microstructure in older adults > 65 years old. We found that the gold standard measure of arterial stiffness, the measure of carotid femoral pulse wave velocity (cfPWV) was associated with axonal fiber organization as reflected by FA and RD rather than demyelination in the white matter regions that have been previously denoted as vulnerable to arterial stiffness. In our second Article, we used the NODDI model to take a further look at the relationship between cfPWV and axonal organization. Our results showed that cfPWV is positively associated with the extracellular water diffusion (ISOVF) which means that arterial stiffness may result in axonal dispersion, lessening the constraint of water directionality along axons. In addition, we found that arterial stiffness is associated with increased fibers density in the corpus callosum as measured by ICVF which could suggest that individuals at higher risk for cognitive decline demonstrate early compensatory mechanisms before the appearance of clinical signs of cognitive decline. Considering the strong interplay between arterial stiffness and decline both in brain structure and function, one can envision a bright future where arterial stiffness would be measured in routine clinical practice in order to identify individuals at higher risk for white matter changes and cognitive decline. Such individuals could benefit from multi-interventions programs aiming to preserve brain structure and function. A cut-off arterial stiffness is thus needed to identify these individuals. Article 3 presents the first estimation of an cfPWV cut-off value at which arterial stiffness impacts the white matter microstructure in older adults. Our results suggested that the current 10 m/s cfPWV cut-off adopted by the European Society of Hypertension may not be the optimal threshold to split individuals into high and low neurovascular risk groups. Instead, our findings suggest that the cfPWV cut-off is more likely to fall around 8.5 m/s. While cfPWV provides excellent prognostic value in adults, it remains unfortunately mainly used in research due to the need of trained experts. Conversely, measure of arterial stiffness index (ASI) using plethysmography is getting increased interest in the last few years due to its simple-to-use approach. In article 4, we investigated the relationship between ASI and pulse pressure (PP), an indirect measure of arterial stiffness, with FA and white matter lesions in participants of the UK Biobank. We found that PP better predicts white matter integrity compared to ASI in participants younger than 75 years old. This finding implies that ASI from plethysmography may not be a reliable measure of arterial stiffness in older adults. Future studies are obviously needed to validate our results, in particular our cfPWV cut-off. Once such cut-off will be validated, the present author envision a bright future where measure of cfPWV will not only be used to help selecting individuals that would most benefit from a multi intervention program aiming to preserve brain integrity, but could also be used to monitor the effect of such intervention

Adapting Swarm Intelligence For The Self-Assembly And Optimization Of Networks

While self-assembly is a fairly active area of research in swarm intelligence and robotics, relatively little attention has been paid to the issues surrounding the construction of network structures. Here, methods developed previously for modeling and controlling the collective movements of groups of agents are extended to serve as the basis for self-assembly or "growth" of networks, using neural networks as a concrete application to evaluate this novel approach. One of the central innovations incorporated into the model presented here is having network connections arise as persistent "trails" left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. The model's effectiveness is demonstrated by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be extended to support and facilitate network self-assembly. Additionally, the traditional self-assembly problem is extended to include the generation of network structures based on optimality criteria, rather than on target structures that are specified a priori. It is demonstrated that endowing the network components involved in the self-assembly process with the ability to engage in collective movements can be an effective means of generating computationally optimal network structures. This is confirmed on a number of challenging test problems from the domains of trajectory generation, time-series forecasting, and control. Further, this extension of the model is used to illuminate an important relationship between particle swarm optimization, which usually occurs in high dimensional abstract spaces, and self-assembly, which is normally grounded in real and simulated 2D and 3D physical spaces

Robust Off- and Online Separation of Intracellularly Recorded Up and Down Cortical States

BACKGROUND: The neuronal cortical network generates slow (<1 Hz) spontaneous rhythmic activity that emerges from the recurrent connectivity. This activity occurs during slow wave sleep or anesthesia and also in cortical slices, consisting of alternating up (active, depolarized) and down (silent, hyperpolarized) states. The search for the underlying mechanisms and the possibility of analyzing network dynamics in vitro has been subject of numerous studies. This exposes the need for a detailed quantitative analysis of the membrane fluctuating behavior and computerized tools to automatically characterize the occurrence of up and down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings from different areas of the cerebral cortex were obtained from both in vitro and in vivo preparations during slow oscillations. A method that separates up and down states recorded intracellularly is defined and analyzed here. The method exploits the crossover of moving averages, such that transitions between up and down membrane regimes can be anticipated based on recent and past voltage dynamics. We demonstrate experimentally the utility and performance of this method both offline and online, the online use allowing to trigger stimulation or other events in the desired period of the rhythm. This technique is compared with a histogram-based approach that separates the states by establishing one or two discriminating membrane potential levels. The robustness of the method presented here is tested on data that departs from highly regular alternating up and down states. CONCLUSIONS/SIGNIFICANCE: We define a simple method to detect cortical states that can be applied in real time for offline processing of large amounts of recorded data on conventional computers. Also, the online detection of up and down states will facilitate the study of cortical dynamics. An open-source MATLAB toolbox, and Spike 2-compatible version are made freely available

Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons

Neural architecture for echo suppression during sound source localization based on spiking neural cell models

Zusammenfassung Diese Arbeit untersucht die biologischen Ursachen des psycho-akustischen Präzedenz Effektes, der Menschen in die Lage versetzt, akustische Echos während der Lokalisation von Schallquellen zu unterdrücken. Sie enthält ein Modell zur Echo-Unterdrückung während der Schallquellenlokalisation, welches in technischen Systemen zur Mensch-Maschine Interaktion eingesetzt werden kann. Die Grundlagen dieses Modells wurden aus eigenen elektrophysiologischen Experimenten an der Mongolischen Wüstenrennmaus gewonnen. Die dabei erstmalig an der Wüstenrennmaus erzielten Ergebnisse, zeigen ein besonderes Verhalten spezifischer Zellen im Dorsalen Kern des Lateral Lemniscus, einer dedizierten Region des auditorischen Hirnstammes. Die dort sichtbare Langzeithemmung scheint die Grundlage für die Echounterdrückung in höheren auditorischen Zentren zu sein. Das entwickelte Model war in der Lage dieses Verhalten nachzubilden, und legt die Vermutung nahe, dass eine starke und zeitlich präzise Hyperpolarisation der zugrundeliegende physiologische Mechanismus dieses Verhaltens ist. Die entwickelte Neuronale Modellarchitektur modelliert das Innenohr und fünf wesentliche Kerne des auditorischen Hirnstammes in ihrer Verbindungsstruktur und internen Dynamik. Sie stellt einen neuen Typus neuronaler Modellierung dar, der als Spike-Interaktionsmodell (SIM) bezeichnet wird. SIM nutzen die präzise räumlich-zeitliche Interaktion einzelner Aktionspotentiale (Spikes) für die Kodierung und Verarbeitung neuronaler Informationen. Die Basis dafür bilden Integrate-and-Fire Neuronenmodelle sowie Hebb'sche Synapsen, welche um speziell entwickelte dynamische Kernfunktionen erweitert wurden. Das Modell ist in der Lage, Zeitdifferenzen von 10 mykrosekunden zu detektieren und basiert auf den Prinzipien der zeitlichen und räumlichen Koinzidenz sowie der präzisen lokalen Inhibition. Es besteht ausschließlich aus Elementen einer eigens entwickelten Neuronalen Basisbibliothek (NBL) die speziell für die Modellierung verschiedenster Spike- Interaktionsmodelle entworfen wurde. Diese Bibliothek erweitert die kommerziell verfügbare dynamische Simulationsumgebung von MATLAB/SIMULINK um verschiedene Modelle von Neuronen und Synapsen, welche die intrinsischen dynamischen Eigenschaften von Nervenzellen nachbilden. Die Nutzung dieser Bibliothek versetzt sowohl den Ingenieur als auch den Biologen in die Lage, eigene, biologisch plausible, Modelle der neuronalen Informationsverarbeitung ohne detaillierte Programmierkenntnisse zu entwickeln. Die grafische Oberfläche ermöglicht strukturelle sowie parametrische Modifikationen und ist in der Lage, den Zeitverlauf mikroskopischer Zellpotentiale aber auch makroskopischer Spikemuster während und nach der Simulation darzustellen. Zwei grundlegende Elemente der Neuronalen Basisbibliothek wurden zur Implementierung als spezielle analog-digitale Schaltungen vorbereitet. Erste Silizium Implementierungen durch das Team des DFG Graduiertenkollegs GRK 164 konnten die Möglichkeit einer vollparallelen on line Verarbeitung von Schallsignalen nachweisen. Durch Zuhilfenahme des im GRK entwickelten automatisierten Layout Generators wird es möglich, spezielle Prozessoren zur Anwendung biologischer Verarbeitungsprinzipien in technischen Systemen zu entwickeln. Diese Prozessoren unterscheiden sich grundlegend von den klassischen von Neumann Prozessoren indem sie räumlich und zeitlich verteilte Spikemuster, anstatt sequentieller binärer Werte zur Informationsrepräsentation nutzen. Sie erweitern das digitale Kodierungsprinzip durch die Dimensionen des Raumes (2 dimensionale Nachbarschaft) der Zeit (Frequenz, Phase und Amplitude) sowie der zeitlichen Dynamik analoger Potentialverläufe. Diese Dissertation besteht aus sieben Kapiteln, welche den verschiedenen Bereichen der Computational Neuroscience gewidmet sind. Kapitel 1 beschreibt die Motivation dieser Arbeit welche aus der Absicht rühren, biologische Prinzipien der Schallverarbeitung zu erforschen und für technische Systeme während der Interaktion mit dem Menschen nutzbar zu machen. Zusätzlich werden fünf Gründe für die Nutzung von Spike-Interaktionsmodellen angeführt sowie deren neuartiger Charakter beschrieben. Kapitel 2 führt die biologischen Prinzipien der Schallquellenlokalisation und den psychoakustischen Präzedenz Effekt ein. Aktuelle Hypothesen zur Entstehung dieses Effektes werden anhand ausgewählter experimenteller Ergebnisse verschiedener Forschungsgruppen diskutiert. Kapitel 3 beschreibt die entwickelte Neuronale Basisbibliothek und führt die einzelnen neuronalen Simulationselemente ein. Es erklärt die zugrundeliegenden mathematischen Funktionen der dynamischen Komponenten und beschreibt deren generelle Einsetzbarkeit zur dynamischen Simulation spikebasierter Neuronaler Netzwerke. Kapitel 4 enthält ein speziell entworfenes Modell des auditorischen Hirnstammes beginnend mit den Filterkaskaden zur Simulation des Innenohres, sich fortsetzend über mehr als 200 Zellen und 400 Synapsen in 5 auditorischen Kernen bis zum Richtungssensor im Bereich des auditorischen Mittelhirns. Es stellt die verwendeten Strukturen und Parameter vor und enthält grundlegende Hinweise zur Nutzung der Simulationsumgebung. Kapitel 5 besteht aus drei Abschnitten, wobei der erste Abschnitt die Experimentalbedingungen und Ergebnisse der eigens durchgeführten Tierversuche beschreibt. Der zweite Abschnitt stellt die Ergebnisse von 104 Modellversuchen zur Simulationen psycho-akustischer Effekte dar, welche u.a. die Fähigkeit des Modells zur Nachbildung des Präzedenz Effektes testen. Schließlich beschreibt der letzte Abschnitt die Ergebnisse der 54 unter realen Umweltbedingungen durchgeführten Experimente. Dabei kamen Signale zur Anwendung, welche in normalen sowie besonders stark verhallten Räumen aufgezeichnet wurden. Kapitel 6 vergleicht diese Ergebnisse mit anderen biologisch motivierten und technischen Verfahren zur Echounterdrückung und Schallquellenlokalisation und führt den aktuellen Status der Hardwareimplementierung ein. Kapitel 7 enthält schließlich eine kurze Zusammenfassung und einen Ausblick auf weitere Forschungsobjekte und geplante Aktivitäten. Diese Arbeit möchte zur Entwicklung der Computational Neuroscience beitragen, indem sie versucht, in einem speziellen Anwendungsfeld die Lücke zwischen biologischen Erkenntnissen, rechentechnischen Modellen und Hardware Engineering zu schließen. Sie empfiehlt ein neues räumlich-zeitliches Paradigma der dynamischen Informationsverarbeitung zur Erschließung biologischer Prinzipien der Informationsverarbeitung für technische Anwendungen.This thesis investigates the biological background of the psycho-acoustical precedence effect, enabling humans to suppress echoes during the localization of sound sources. It provides a technically feasible and biologically plausible model for sound source localization under echoic conditions, ready to be used by technical systems during man-machine interactions. The model is based upon own electro-physiological experiments in the mongolian gerbil. The first time in gerbils obtained results reveal a special behavior of specific cells of the dorsal nucleus of the lateral lemniscus (DNLL) - a distinct region in the auditory brainstem. The explored persistent inhibition effect of these cells seems to account for the base of echo suppression at higher auditory centers. The developed model proved capable to duplicate this behavior and suggests, that a strong and timely precise hyperpolarization is the basic mechanism behind this cell behavior. The developed neural architecture models the inner ear as well as five major nuclei of the auditory brainstem in their connectivity and intrinsic dynamics. It represents a new type of neural modeling described as Spike Interaction Models (SIM). SIM use the precise spatio-temporal interaction of single spike events for coding and processing of neural information. Their basic elements are Integrate-and-Fire Neurons and Hebbian synapses, which have been extended by specially designed dynamic transfer functions. The model is capable to detect time differences as small as 10 mircrosecondes and employs the principles of coincidence detection and precise local inhibition for auditory processing. It consists exclusively of elements of a specifically designed Neural Base Library (NBL), which has been developed for multi purpose modeling of Spike Interaction Models. This library extends the commercially available dynamic simulation environment of MATLAB/SIMULINK by different models of neurons and synapses simulating the intrinsic dynamic properties of neural cells. The usage of this library enables engineers as well as biologists to design their own, biologically plausible models of neural information processing without the need for detailed programming skills. Its graphical interface provides access to structural as well as parametric changes and is capable to display the time course of microscopic cell parameters as well as macroscopic firing pattern during simulations and thereafter. Two basic elements of the Neural Base Library have been prepared for implementation by specialized mixed analog-digital circuitry. First silicon implementations were realized by the team of the DFG Graduiertenkolleg GRK 164 and proved the possibility of fully parallel on line processing of sounds. By using the automated layout processor under development in the Graduiertenkolleg, it will be possible to design specific processors in order to apply theprinciples of distributed biological information processing to technical systems. These processors differ from classical von Neumann processors by the use of spatio temporal spike pattern instead of sequential binary values. They will extend the digital coding principle by the dimensions of space (spatial neighborhood), time (frequency, phase and amplitude) as well as the dynamics of analog potentials and introduce a new type of information processing. This thesis consists of seven chapters, dedicated to the different areas of computational neuroscience. Chapter 1: provides the motivation of this study arising from the attempt to investigate the biological principles of sound processing and make them available to technical systems interacting with humans under real world conditions. Furthermore, five reasons to use spike interaction models are given and their novel characteristics are discussed. Chapter 2: introduces the biological principles of sound source localization and the precedence effect. Current hypothesis on echo suppression and the underlying principles of the precedence effect are discussed by reference to a small selection of physiological and psycho-acoustical experiments. Chapter 3: describes the developed neural base library and introduces each of the designed neural simulation elements. It also explains the developed mathematical functions of the dynamic compartments and describes their general usage for dynamic simulation of spiking neural networks. Chapter 4: introduces the developed specific model of the auditory brainstem, starting from the filtering cascade in the inner ear via more than 200 cells and 400 synapses in five auditory regions up to the directional sensor at the level of the auditory midbrain. It displays the employed parameter sets and contains basic hints for the set up and configuration of the simulation environment. Chapter 5: consists of three sections, whereas the first one describes the set up and results of the own electro-physiological experiments. The second describes the results of 104 model simulations, performed to test the models ability to duplicate psycho-acoustical effects like the precedence effect. Finally, the last section of this chapter contains the results of 54 real world experiments using natural sound signals, recorded under normal as well as highly reverberating conditions. Chapter 6: compares the achieved results to other biologically motivated and technical models for echo suppression and sound source localization and introduces the current status of silicon implementation. Chapter 7: finally provides a short summary and an outlook toward future research subjects and areas of investigation. This thesis aims to contribute to the field of computational neuroscience by bridging the gap between biological investigation, computational modeling and silicon engineering in a specific field of application. It suggests a new spatio-temporal paradigm of information processing in order to access the capabilities of biological systems for technical applications

Neural Field Models: A mathematical overview and unifying framework

Rhythmic electrical activity in the brain emerges from regular non-trivial interactions between millions of neurons. Neurons are intricate cellular structures that transmit excitatory (or inhibitory) signals to other neurons, often non-locally, depending on the graded input from other neurons. Often this requires extensive detail to model mathematically, which poses several issues in modelling large systems beyond clusters of neurons, such as the whole brain. Approaching large populations of neurons with interconnected constituent single-neuron models results in an accumulation of exponentially many complexities, rendering a realistic simulation that does not permit mathematical tractability and obfuscates the primary interactions required for emergent electrodynamical patterns in brain rhythms. A statistical mechanics approach with non-local interactions may circumvent these issues while maintaining mathematically tractability. Neural field theory is a population-level approach to modelling large sections of neural tissue based on these principles. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. We elucidate a mathematical framework in which neural field models can be derived, highlighting the many significant inherited assumptions that exist in the current literature, so that their validity may be considered in light of further developments in both mathematical and experimental neuroscience.Comment: 55 pages, 10 figures, 2 table