The understanding of human brain mechanisms has captured the imagination of scientists for ages. From the quantitative perspective, there is evidence that damages to brain structure affect brain function and, as a consequence, cognitive aspects. As there is evidence that brain structure might be affected by altered cognition. However, the complex interplay between the human brain and the mind remains still poorly understood. This fact has important clinical consequences, limiting applications devoted to the prevention and treatment of brain diseases. In the present thesis, we aim to enhance our understanding of human brain mechanisms by means of an integrated and data-driven approach, by adopting a systemic perspective and leveraging on tools from computational and network neuroscience. We successfully enhance the state of the art of computational neuroscience in several manners. Firstly, we inspect human cognition by focusing on the geometric exploration of concepts in the human mind to build new datadriven metrics to complement the neurological assessment and to confirm Alzheimer’s disease diagnosis. We formalize a new stochastic process, the potential-driven random walk, able to model the trade-off between exploitation and exploration of network structure, by accounting for local and global information, providing a flexible tool to span from random walk to shortestpath based navigation. Probing the interplay between brain structure and dynamics by means of its Von Neumann entropy, we develop a new framework for the multiscale analysis of the human connectome, which is effective for discerning between healthy conditions and Alzheimer’s disease. Finally, by integrating data from the human brain structural connectivity, its functional response errors as measured by Direct Electrical Stimulation and semantic selectivity, we propose a new procedure for mapping the human brain triadic nature, thus providing a model-oriented bridge between the human brain and mind. Besides shedding more light on human brain functioning, our findings offer original and promising clues to develop integrated biomarkers for Alzheimer’s disease detection, with the potential of extension for applications to other neurodegenerative diseases and psychiatric disorders.

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Biophysically detailed representations of neural network models provide substantial insight to underlying neural processing mechanisms in the auditory systems of the brain. For simple biological systems the behavior can be represented by simple equations or flow charts. But for complex systems, more detailed descriptions of individual neurons and their synaptic connectivity are typically required. Creating extensive network models allows us to test hypotheses, apply specific manipulations that cannot be done experimentally and provide supporting evidence for experimental results. Several studies have been made on establishing realistic models of the cochlear nucleus (Manis and Campagnola, 2018; Eager et al., 2004), the part of the brainstem where sound signals enter the brain, both on individual neuron and networked structure levels. These models are based on both in vitro and in vivo physiological data, and they successfully demonstrate certain aspects of the neural processing of sound signals. Even though these models have been tested with tone bursts and isolated phonemes, the representation of speech in the cochlear nucleus and how it may support robust speech intelligibility remains to be explored with these detailed biophysical models. In this study, the basis of creating a biophysically detailed model of microcircuits in the cochlear nucleus is formed following the approach of Manis and Campagnola (2018). The focus of this thesis is more on bushy cell microcircuits. We have updated Manis and Campagnola (2018) model to take inputs from the new phenomenological auditory periphery model of Bruce et al. (2018). Different cell types in the cochlear nucleus are modelled by detailed cell models of Rothman and Manis (2003c) and updated Manis and Campagnola (2018) cell models. Networked structures are built out of them according to published anatomical and physiological data. The outputs of these networked structures are used to create post-stimulus-time-histograms (PSTH) and response maps to investigate the representation of tone bursts and average localized synchronized rate (ALSR) of phoneme 'e' and are compared to published physiological data (Blackburn and Sachs, 1990). / Thesis / Master of Applied Science (MASc)

In the central nervous system, most of the processes ranging from ion channels to neuronal networks occur in a closed loop, where the input to the system depends on its output. In contrast, most experimental preparations and protocols operate autonomously in an open loop and do not depend on the output of the system. Real-time software technology can be an essential tool for understanding the dynamics of many biological processes by providing the ability to precisely control the spatiotemporal aspects of a stimulus and to build activity-dependent stimulus-response closed loops. So far, application of this technology in biological experiments has been limited primarily to the dynamic clamp, an increasingly popular electrophysiology technique for introducing artificial conductances into living cells. Since the dynamic clamp combines mathematical modeling with electrophysiology experiments, it inherits the limitations of both, as well as issues concerning accuracy and stability that are determined by the chosen software and hardware. In addition, most dynamic clamp systems to date are designed for specific experimental paradigms and are not easily extensible to general real-time protocols and analyses. The long-term goal of this research is to develop a suite of real-time tools to evaluate the performance, improve the efficacy, and extend the capabilities of the dynamic clamp technique and real-time neural electrophysiology. We demonstrate a combined dynamic clamp and modeling approach for studying synaptic integration, a software platform for implementing flexible real-time closed-loop protocols, and the potential and limitations of Kalman filter-based techniques for online state and parameter estimation of neuron models.

Neuronale Adaptation dient dazu, eine Sinnesbahn kurzfristig an die aktuelle Umgebung des Tieres anzupassen. Ihr zeitlicher Verlauf lässt sich in der Antwort einzelner Nervenzellen direkt beobachten. Der Adaptation unterliegen eine Vielzahl verschiedener Mechanismen, die über die gesamte Sinnesbahn verteilt sein können. In der vorliegenden Arbeit wurde der Versuch unternommen, diese unterschiedlichen Betrachtungsebenen zusammenzuführen. Dazu wurden mehrere experimentelle und theoretische Studien durchgeführt. In zwei der vorgestellten Studien wurden Kombinationen aus Strominjektionen und akustischen Reizen verwendet, um intrinsische Adaptation von Netzwerkeffekten zu trennen. Dabei ergab sich in einer experimentellen Studie am auditorischen System der Heuschrecke, dass die Adaptationsmechanismen, die in verschiedenen Teilen der Hörbahn rekrutiert werden, sehr stark von Identität und Funktion der jeweils untersuchten Nervenzelle abhängen. Ähnliche Methoden ermöglichten es, im auditorischen System der Grille präsynaptische Hemmung als Substrat für die wichtige mathematische Operation der Division zu identifizieren. Zusätzlich wurden Modellierungen durchgeführt, bei denen die Frage bearbeitet wurde, wo Adaptation in der Hörbahn wirken sollte, bezogen auf zwei verschieden Aufgaben: die Lokalisation eines Signals und die neuronale Abbildung dessen zeitlicher Struktur. Die Ergebnisse dieser Studie deuten darauf hin, dass die Anforderungen für diese beiden Aufgaben sehr unterschiedliche sind. In einer vierten Studie wurde untersucht, ob die Adaptation in einem auditorischen Interneuron der Grille dazu dient, die gesamte sensorische Umgebung gut abzubilden, oder ob durch die Adaptation eine Abtrennung des jeweils lautesten Signals erreicht werden kann. Zusammenfassend lässt sich sagen, dass sowohl die Adaptationsmechanismen, als auch deren genaue Platzierung innerhalb der sensorischen Bahn wesentlich für Sinnesleistungen sind. / Neural adaptation serves to adjust the sensory pathway to the current environment of an animal. While the effect and time course of adaptation can be observed directly within single cells, its underlying cause is a combination of many different mechanisms spread out along the sensory pathway. The present work has the objective to unite these different levels of understanding of the term adaptation. In order to do so, several experimental and theoretical studies were carried out. In two of these studies, a combination of current injection and auditory stimulation was used, in order to disentangle intrinsic adaptation from network effects. In one of the studies, carried out in the auditory system of locusts, it was revealed that the mechanisms behind adaptation that are activated within different parts of the auditory system depend critically on identity and function of the cell under study. Similar methods enabled the identification of presynaptic inhibition as a possible mechanisms behind the important mathematical operation of division in the auditory system of crickets. Additionally, a modeling study pursued the question, where adaption should work in the auditory system from the perspective of two different tasks of sensory processing: identification of a signal and localization of its source. The results obtained from the model suggest conflicting demands for these two tasks and also present a solution of this conflict. In a fourth study, it was asked wether adaptation in the auditory system of crickets serves to guarantee optimal representation of the entire sensory environment or if it helps to separate one most important signal from the background. In summary, not only which mechanisms of adaptation are at work is of crucial importance for sensory processing, but also the exact placement of these along the pathway.

Das Ziel von computational neuroscience ist, neuronale Transformationen zu beschreiben und deren Mechanismen und Funktionen zu beleuchten. Diese Doktorarbeit kombiniert Experiment, Datenanalyse und Modelle um neuronale Kodierung anhand des auditorischen Systems von Feldheuschrecke und Grille zu erforschen. Der erste Teil befasst sich mit der neuronalen Repräsentation von Balzsignalen in Feldheuschrecken. In Rezeptoren ist die Kodierung dieser Signale homogen - alle Neuronen bilden den Reiz gleich ab. In nachgeschalteten Zellen wird die Kodierung spärlicher, sowohl auf Ebene der Zeit als auch der Zellpopulation. Es entsteht ein labeled line code, bei dem unterschiedliche Nervenzellen unterschiedliche Merkmale des Stimulus abbilden. Dieser Transformation liegt eine nichtlineare Kombination von mehreren Stimulusmerkmalen zu Grunde. Die erhöhte Spezifizität von Neuronen dritter Ordnung ermöglicht eine einfache Art der Musterklassifikation, bei der die Zeitpunkte bestimmter Reizelemente innerhalb des Signals ignoriert werden können. Die beschriebene Reiztransformation repräsentiert einen Mechanismus für die Erkennung zeitlich redundanter Kommunikationssignale, wie sie von vielen Insekten produziert werden. Im zweiten Teil wird gezeigt, dass die spektrale und zeitliche Abstimmung von Neuronen zweiter Ordnung bei Grillen von der Komplexität des Reizes abhängt. Während die Abstimmung für Reize mit nur einer Trägerfrequenz breit ist, führen Reize mit mehreren Trägerfrequenzen zu einer Schärfung. Hierdurch kann Information über einzelne Komponenten eines komplexen Signals in der Kodierung erhalten werden. Ein statisches Netzwerkmodell zeigt, dass diese adaptive Abstimmung mit Mechanismen erzeugt werden kann, die in Nervensystemen vieler Organismen vorkommen. Wie diese Doktorabeit zeigt, vereinen Insekten einfach aufgebaute und gut zugängliche Nervensysteme mit komplexen Reiztransformationen. Dies macht sie zu produktiven Modellorganismen für die Neurowissenschaften. / The goal of computational neuroscience is to describe the stimulus transformations performed by neural systems and to elucidate their mechanisms and functions. This thesis combines experiment, data analysis and theoretical modeling to explore neural coding in the small auditory systems of grasshoppers and crickets. The first part deals with the transformation of the neural representation of courtship signals in grasshoppers. The code in auditory receptors is relatively homogeneous. That is, all neurons represent a very similar stimulus feature. Representation in higher-order neurons leads to an increase of temporal and population sparseness. This creates a labeled-line population code where different neurons represent different and specific stimulus features. Sparseness in the system increases through a nonlinear combination of two stimulus features. This transformation enables a simple mode of pattern classification, which ignores the timing of individual features and relies only on their average values during a signal. The transformation can therefore facilitate the recognition of the long, temporally redundant communication signals produced by grasshoppers and other insects. The second part shows that spectral and temporal tuning of second-order neurons in crickets strongly depends on the complexity of the stimulus. While tuning is relatively broad for single-carrier stimuli, signals containing multiple carrier frequencies lead to a sharpening of the tuning. This sharpening preserves information about individual components of a complex stimulus. A network model revealed that such adaptive tuning can be implemented in a static network with mechanisms that are ubiquitous in many neural systems. In summary, this study shows that the nervous systems of insects combine a relatively simple structure with complex stimulus transformations. This renders them empirically accessible and suitable model systems for computational neuroscience.

<p>Our ability to control unstable objects highlights the sophistication of voluntary motor behaviour. In this thesis, we used an inverted pendulum (i.e., stick) balancing paradigm to investigate the task, learning and context-dependent attributes of unstable object control. We hypothesized that learning would mediate the functional integration of posture and upper limb dynamics and expected changes in the task demand and context to be reflected in the control of posture and the upper limb. We found that training increased the average length of balancing trials and applied this result to further investigate the circumstantial properties of unstable object control.</p> <p>We investigated the temporal structure of posture and upper limb dynamics using statistical and nonlinear time series analysis. We demonstrated that subjects used an intermittent strategy to control the inverted pendulum (Chapters 3 and 5) and found that motor learning modulated the statistical and spatiotemporal attributes of posture (Chapter 5) and upper limb displacements (Chapters 2, 3 and 5). We confirmed the balance control strategy was intermittent by showing that posture and upper limb time series are composed of two independent timescale components: a fast component linked to small stochastic displacements and a slow component related to feedback control (Chapters 3, 4 and 5). The interplay between timescale components was affected by the balancing context (Chapter 3) and task demand (Chapter 4).</p> <p>Chapter 5 investigated the acquisition of individual and coupled posture-upper limb control mechanisms. We found that motor learning involved two independent adaptation processes. The first process modified the timescale composition of posture and upper limb displacements and was followed by incremental changes in the occurrence and duration of correlated posture-upper limb trajectories. In Chapter 6, we investigated learning-mediated changes in multijoint coordination and control. Motor learning led to the flexible, error-compensating recruitment of individual joints and we showed that the preferential constraint of destabilizing joint angle variance was the putative mechanism underlying performance.</p> <p>This thesis performed a detailed examination of unstable object control mechanisms. The undertaken studies have provided knowledge about the acquisition and adaptation of control mechanisms at multiple levels of the motor system. Our data provide convergent evidence that the control mechanisms governing complex human balancing tasks are intermittent and modulated by the task and context.</p> / Doctor of Philosophy (PhD)

In this thesis we model two very different movement-related neuronal circuits, both of which produce oscillatory patterns of activity. In one case we study oscillatory activity in the basal ganglia under both normal and Parkinsonian conditions. First, we used a detailed Hodgkin-Huxley type spiking model to investigate the activity patterns that arise when oscillatory cortical input is transmitted to the globus pallidus via the subthalamic nucleus. Our model reproduced a result from rodent studies which shows that two anti-phase oscillatory groups of pallidal neurons appear under Parkinsonian conditions. Secondly, we used a population model of the basal ganglia to study whether oscillations could be locally generated. The basal ganglia are thought to be organised into multiple parallel channels. In our model, isolated channels could not generate oscillations, but if the lateral inhibition between channels is sufficiently strong then the network can act as a rhythm-generating ``pacemaker'' circuit. This was particularly true when we used a set of connection strength parameters that represent the basal ganglia under Parkinsonian conditions. Since many things are not known about the anatomy and electrophysiology of the basal ganglia, we also studied oscillatory activity in another, much simpler, movement-related neuronal system: the spinal cord of the Xenopus tadpole. We built a computational model of the spinal cord containing approximately 1,500 biologically realistic Hodgkin-Huxley neurons, with synaptic connectivity derived from a computational model of axon growth. The model produced physiological swimming behaviour and was used to investigate which aspects of axon growth and neuron dynamics are behaviourally important. We found that the oscillatory attractor associated with swimming was remarkably stable, which suggests that, surprisingly, many features of axonal growth and synapse formation are not necessary for swimming to emerge. We also studied how the same spinal cord network can generate a different oscillatory pattern in which neurons on both sides of the body fire synchronously. Our results here suggest that under normal conditions the synchronous state is unstable or weakly stable, but that even small increases in spike transmission delays act to stabilise it. Finally, we found that although the basal ganglia and the tadpole spinal cord are very different systems, the underlying mechanism by which they can produce oscillations may be remarkably similar. Insights from the tadpole model allow us to predict how the basal ganglia model may be capable of producing multiple patterns of oscillatory activity.

It is well established that, following adaptation, cells adjust their sensitivity to reflect the global stimulus conditions. Two recent studies in guinea pig inferior colliculus (IC, Dean, Harper &amp; McAlpine 2005) and rat barrel cortex (Garcia-Lazaro, Ho, Nair &amp; Schnupp 2007) found that neural stimulus-response functions were displaced laterally in a manner that was dependent on the mean adapting stimulus. However, the direction of gain change, following adaptation to variance, was in contradiction to Information Theory, which predicts a decrease in gain with increased stimulus variance. On further analysis of the experimental data, presented within this thesis, it was revealed that the adaptive gain changes to global stimulus variance were, in fact, in the direction predicted by Information Theory. However, following adaptation to global mean amplitude, neural threshold was displaced to centre the SRF on inputs that were located on the edge of the stimulus distribution. It was found that adaptation scaled neural output such that the relationship between firing rate and local, as opposed to global, differences in stimulus amplitude was maintained; with the majority of cells responding to large differences in stimulus amplitude, on the 40ms scale. A small majority of cells responded to step-size differences, in amplitude, of either direction and were classed as novelty preferring. Adaptation to global mean was replicated in model neuron with spike-rate adaptation and tonic inhibition, which increased with stimulus mean. Adaptation to stimulus variance was replicated in three models 1: By increasing, in proportion to stimulus variance, background, excitatory and inhibitory firing rates in a balanced manner (Chance, Abbott &amp; Reyes 2002), 2: A model of asymmetric synaptic depression (Chelaru &amp; Dragoi 2008) and 3: a model combining non-linear input with synaptic depression. The results presented, within this thesis, demonstrate that neurons change their coding strategies depending upon the global levels of mean and variance within the sensory input. Under low noise conditions, neurons act as deviation detectors, i.e. are primed to respond to large changes in the stimulus on the tens of millisecond; however, under conditions of increased noise switch their encoding strategy in order to compute the full range of the stimulus distribution through adjusting neural gain.