Sinnessysteme erfassen und verarbeiten staendig die vielfaeltigen und komplexen Reize der Umwelt. Um die funktionellen Eigenschaften eines solchen Systems zu untersuchen, verwendet man jedoch oft relativ einfache, abstrakte Reize. Diese Reize erlauben aber meist nicht, die Funktion des Systems im Verhaltenskontext zu interpretieren. Ferner erhaelt man durch einfache Reize im allgemeinen eine unvollstaendige Beschreibung des Systems. Innerhalb dieser Arbeit zeige ich exemplarisch am Beispiel von auditorischen Rezeptorneuronen von Heuschrecken, wie man natuerliche Stimuli einsetzen kann, um die sensorische Codierung zu untersuchen.Heuschrecken verwenden akustische Kommunikation zur Partnerfindung und -auswahl. Dabei sind die Weibchen hochselektiv bei der Wahl eines Maennchens. Von besonderem Interesse ist daher, inwieweit Informationen ueber Unterschiede zwischen Maennchengesaengen durch die auditorischen Rezeptoren des Weibchens erhalten werden. Wie in der Arbeit gezeigt wird, liefern selbst einzelne Rezeptorneuronen hinreichend Information, um selbst kleine Unterschiede zwischen den Maennchengesaengen zu erkennen. Diese erstaunliche Aufloesung der Gesaenge dient vermutlich der Auswahl von genetisch hochwertigen Partnern. Ferner wird gezeigt, dass auditorische Rezeptoren nicht allgemein viel Information ueber Stimuli liefern, sondern auf spezifische Zeitskalen und Strukturen der natuerlichen Stimuli optimiert sind. Falls sensorische Systeme generell gut auf die jeweilig verhaltensrelevanten Stimuli abgestimmt sind, so kann man diese Stimuli auch automatisch finden. Im letzten Teil der Arbeit wird ein Online-Algorithmus vorgestellt, der dieses Ziel unter Verwendung informationstheoretischer Prinzipien erreicht. Dieser Algorithmus kann in Zukunft dazu dienen, die Effizienz elektrophysiologischer Experimente in beliebigen Systemen zu erhoehen. / In their natural environment, sensory systems process a wealth of complex stimuli. In contrast, most experimental tests of sensory systems employ simple stimuli that can be described by one or two parameters. However, these simple stimuli do usually not allow to relate the function of a specific system to an animal's behaviour. Furthermore, in many cases a complete characterisation of a sensory system cannot be achieved by simple stimuli alone. Within this thesis, I demonstrate how one can employ natural stimuli to study aspects of sensory coding. Grasshoppers use acoustic communication for mate detection and selection. Females show preferences for certain "qualities" of the signals produced by different conspecific males. In this thesis, I investigated how much information female grasshoppers obtain about differences between the mating songs of males. Already single auditory receptor neurons of female grasshoppers encode sufficient information to distinguish even fine variations of male songs. Presumably, this astonishing resolution is needed to single out males of high genetic quality. Furthermore, I show that the ensemble of stimuli that best explores the coding regime of a given receptor has features and time scales that are typical for grasshopper songs. If a close match between the behaviourally relevant stimuli and the sensory system is an evolutionary design principle, then one can extract the relevant stimuli from a given system without prior knowledge. In the last part of the thesis, an online algorithm is introduced, that achieves this goal using information-theoretic principles. This algorithm might help to improve the performance of experiments within the limited time of an electrophysiological recording session.

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.

L’évolution des techniques d’acquisition de l’activité neuronale permet désormais d'enregistrer simultanément jusqu’à plusieurs centaines de neurones dans le cortex ou dans la rétine. L’analyse de ces données nécessite des méthodes mathématiques et numériques pour décrire les corrélations spatiotemporelles de la population neuronale. Une méthode couramment employée est basée sur le principe d’entropie maximale. Dans ce cas, le produit N×R, où N est le nombre de neurones et R le temps maximal considéré dans les corrélations, est un paramètre crucial. Les méthodes de physique statistique usuelles sont limitées aux corrélations spatiales avec R = 1 (Ising) alors que les méthodes basées sur des matrices de transfert, permettant l’analyse des corrélations spatio-temporelles (R > 1), sont limitées à N×R≤20. Dans une première partie, nous proposons une version modifiée de la méthode de matrice de transfert, basée sur un algorithme de Monte-Carlo parallèle, qui nous permet d’aller jusqu’à N×R=100. Dans la deuxième partie, nous présentons la bibliothèque C++ Enas, dotée d’une interface graphique développée pour les neurobiologistes. Enas offre un environnement hautement interactif permettant aux utilisateurs de gérer les données, effectuer des analyses empiriques, interpoler des modèles statistiques et visualiser les résultats. Enfin, dans une troisième partie, nous testons notre méthode sur des données synthétiques et réelles (rétine, fournies par nos partenaires biologistes). Notre analyse non exhaustive montre l’avantage de considérer des corrélations spatio-temporelles pour l’analyse des données rétiniennes; mais elle montre aussi les limites des méthodes d’entropie maximale. / Recent experimental advances have made it possible to record up to several hundreds of neurons simultaneously in the cortex or in the retina. Analyzing such data requires mathematical and numerical methods to describe the spatio-temporal correlations in population activity. This can be done thanks to Maximum Entropy method. Here, a crucial parameter is the product N×R where N is the number of neurons and R the memory depth of correlations (how far in the past does the spike activity affects the current state). Standard statistical mechanics methods are limited to spatial correlation structure with R = 1 (e.g. Ising model) whereas methods based on transfer matrices, allowing the analysis of spatio-temporal correlations, are limited to NR ≤ 20. In the first part of the thesis we propose a modified version of the transfer matrix method, based on the parallel version of the Montecarlo algorithm, allowing us to go to NR=100. In a second part we present EnaS, a C++ library with a Graphical User Interface developed for neuroscientists. EnaS offers highly interactive tools that allow users to manage data, perform empirical statistics, modeling and visualizing results. Finally, in a third part, we test our method on synthetic and real data sets. Real data set correspond to retina data provided by our partners neuroscientists. Our non-extensive analysis shows the advantages of considering spatio-temporal correlations for the analysis of retina spike trains, but it also outlines the limits of Maximum Entropy methods.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-14T16:42:54Z No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) / Made available in DSpace on 2018-08-14T16:42:54Z (GMT). No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) Previous issue date: 2009-11-06 / Neste trabalho é proposto o desenvolvimento de uma rede neuronial com aprendizagem não supervisionada, para modelar a organização topográfica do córtex visual primário. Para isto, estuda-se o comportamento dos campos receptivos do córtex visual primário(V1), e, para o modelamento da rede utilizam-se os conceitos de divergência local e de interação entre neurônios vizinhos, bem como da característica de não linearidades dos neurônios. Para treinamento da rede desenvolveu-se um algoritmo de ponto fixo. / In this work it is proposed an unsupervised neural network model, which seems biologically plausible in modeling the primary visual cortex (V1). It is, also, studied de behavior of the receptive fields of V1. In order to modeling the net it was used the concepts of local discrepancy and interactions between neighbor neurons, as well the non-linearity characteristics of neurons. It was designed a fixed-point algorithm to train the neural network.

This work presents biologically motivated neural network model which works as an auto-associative memory. Architecture of the presented model is similar to the architecture of the Hopfield network which might be similar to some parts of the hippocampal network area CA3 (Cornu Amonis). Patterns learned and retrieved are not static but they are periodically repeating sequences of sparse synchronous activities. Patterns were stored to the network using the modified Hebb rule adjusted to store cyclic sequences. Capacity of the model is analyzed together with the numerical simulations. The model is further extended with short term potentiation (STP), which is forming the essential part of the successful pattern recall process. The memory capacity of the extended version of the model is highly increased. The joint version of the model combining both approaches is discussed. The model might be able to retrieve the pattern in short time interval without STP (fast patterns) or in a longer time period utilizing STP (slow patterns). We know from our everyday life that some patterns could be recalled promptly and some may need much longer time to reveal. Keywords auto-associative neural network, Hebbian learning, neural coding, memory, pattern recognition, short-term potentiation 1

Le gyrus denté occupe une position clé au sein du lobe temporal des mammifères en constituant le point de contrôle entre le système néocortical et le système hippocampique. Considéré comme la porte de l'hippocampe, le gyrus denté filtre les activités excitatrices en provenance du cortex entorhinal grâce à la décharge éparse des cellules granulaires. Ce type de codage neuronal lui confère également un rôle déterminant dans les mécanismes de l'apprentissage et de la mémoire lors de la distinction d'évènements similaires mais différents, en permettant la décorrélation des patrons d'activité corticale. Grâce à un ensemble de propriétés structurales et fonctionnelles, les cellules granulaires du gyrus denté génèrent des évènements synaptiques extrêmement rapides restreignant leur fenêtre temporelle d'intégration et leur permettant de jouer le rôle de détecteurs de coïncidence. Au cours d'une épilepsie du lobe temporal (ELT), l'hippocampe présente d'importantes altérations de codage neuronal qui pourraient participer aux troubles cognitifs décrits chez les patients et les modèles animaux. Dans ces conditions pathologiques, les axones des cellules granulaires du gyrus denté (les fibres moussues) bourgeonnent et établissent des synapses aberrantes au niveau d'autres cellules granulaires, créant ainsi un puissant réseau excitateur récurrent. Ces fibres moussues récurrentes convertissent la nature de la transmission glutamatergique car elles opèrent via des récepteurs kaïnate générant des potentiels post-synaptiques à cinétique lente, absents en condition contrôle. / The dentate gyrus plays a major role at the gate of the hippocampus, filtering incoming information from the entorhinal cortex. A fundamental coding property of dentate granule cells (DGCs) is their sparse firing. Indeed, they behave as a coincidence detector due to the fast kinetics of excitatory synaptic events restricting integration of afferent inputs to a narrow time window. In temporal lobe epilepsy (TLE), the hippocampus displays important coding alterations that may play a role in cognitive impairments described in patients and animal models. However, the cellular mechanisms remain poorly understood. In animal models of TLE and human patients, neuronal tissue undergoes major reorganization; some neurons die whereas others, which are severed in their inputs or outputs, sprout and form novel aberrant connections. This phenomenon, called reactive plasticity, is well documented in the dentate gyrus where DGC axons (the mossy fibres) sprout and create a powerful excitatory network between DGCs. We recently showed that in addition to the axonal rewiring, recurrent mossy fibres convert the nature of glutamatergic transmission in the dentate gyrus because they operate via long-lasting kainate receptor (KAR)-mediated EPSPs (EPSPKA) not present in the naive condition.

<div>Hearing loss hinders the communication ability of many individuals despite state-of-the-art interventions. Animal models of different hearing-loss etiologies can help improve the clinical outcomes of these interventions; however, several gaps exist. First, translational aspects of animal models are currently limited because anatomically and physiologically specific data obtained from animals are analyzed differently compared to noninvasive evoked responses that can be recorded from humans. Second, we lack a comprehensive understanding of the neural representation of everyday sounds (e.g., naturally spoken speech) in real-life settings (e.g., in background noise). This is even true at the level of the auditory nerve, which is the first bottleneck of auditory information flow to the brain and the first neural site to exhibit crucial effects of hearing-loss. </div><div><br></div><div>To address these gaps, we developed a unifying framework that allows direct comparison of invasive spike-train data and noninvasive far-field data in response to stationary and nonstationary sounds. We applied this framework to recordings from single auditory-nerve fibers and frequency-following responses from the scalp of anesthetized chinchillas with either normal hearing or noise-induced mild-moderate hearing loss in response to a speech sentence in noise. Key results for speech coding following hearing loss include: (1) coding deficits for voiced speech manifest as tonotopic distortions without a significant change in driven rate or spike-time precision, (2) linear amplification aimed at countering audiometric threshold shift is insufficient to restore neural activity for low-intensity consonants, (3) susceptibility to background noise increases as a direct result of distorted tonotopic mapping following acoustic trauma, and (4) temporal-place representation of pitch is also degraded. Finally, we developed a noninvasive metric to potentially diagnose distorted tonotopy in humans. These findings help explain the neural origins of common perceptual difficulties that listeners with hearing impairment experience, offer several insights to make hearing-aids more individualized, and highlight the importance of better clinical diagnostics and noise-reduction algorithms. </div>

Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.

Every individual is born with different natural competencies that can be honed by both voluntary and involuntary environmental stimuli. The response our genotype decides to make, if any, towards those stimuli, determines how well our competencies develop. Each person’s coding and variations of genes will result in unique qualities in their phenotype, or physical structure. As a result, a person has various traits that are displayed through their behavior. DNA is genetically shown to express itself through traits by up to 75%. This leaves a sort of buffer of around 25%. This region is available for us to adapt to our environmental stimuli. Your innate qualities will not reach their full potential without stimulation from the environment, in a leadership case, with education and training and therefore it can be argued that environmental exposure is necessary to fully expose the potentials and capabilities of an individual, rather than instill a new skill or develop a talent that was not existent before. Innate leadership is not a permanent state, on the contrary, it is a continuously adaptive situation demanding contextual evolutionary changes or resignation from the subject occupying the role. When the needs and demands of a society or era outweigh the relevance of the innate leaders' traits and competencies, an evolution of leadership is needed to maintain a positive relationship between all parties involved. As a result, the innate leader will begin to lose their innateness in their role and unless they evolve and adapt (because the two actions are not the same) to new contextual needs, their tenure as leader will begin to be detrimental and counter-functional. What we want to put forward is a real, universal and constructive understanding of what makes a human happy, motivated and productive and how an innate person in context is a much better solution in the short and long run, for those around them when put to a task.

Over the last decade advances in multineuron simultaneous recording techniques have produced huge amounts of data. This has led to the investigation of probable temporal relationships between spike times of neurons as manifestations of the underlying network structure. But the huge dimensionality of data makes the search for patterns difficult. Although this difficulty may be surpassed by employing massive computing resources, understanding the significance and relation of these temporal patterns to the underlying network structure and the causative activity is still difficult. To find such relationships in networks of excitatory neurons, a simplified network structure of feedforward chains called "Synfire chains" has been frequently employed. But in a recurrently connected network where activity from feedback connections is comparable to the feedforward chain, the basic assumptions underlying synfire chains are violated. In the first part of this thesis we propose the first-spike-latency based analysis as a low complexity method of studying the temporal relationships between neurons. Firstly, spike latencies being temporal delays measured at a particular epoch of time (onset of activity after a quiescent period) are a small subset of all the temporal information available in spike trains, thereby hugely reducing the amount of data that needs to be analyzed. We also define for the first time, "Synconset waves and chains" as a sequence of first-spike-times and the causative neuron chain. Using simulations, we show the efficacy of the synconset paradigm in unraveling feedforward chains of excitatory neurons even in a recurrent network. We further create a framework for going back and forth between network structure and the observed first-spike-latency patterns. To quantify these associations between network structure and dynamics we propose a likelihood measure based on Bayesian reasoning. This quantification is agnostic to the methods of association used and as such can be used with any of the existing approaches. We also show the benefits of such an analysis when the recorded data is subsampled, as is the case with most physiological recordings. In the subsequent part of our thesis we show two sample applications of first-spike-latency analysis on data acquired from multielectrode arrays. Our first application dwells on the intricacies of extracting first-spike-latency patterns from multineuron recordings using recordings of glutamate injured cultures. We study the significance of these patterns extracted vis-a-vis patterns that may be obtained from exponential spike latency distributions and show the differences between patterns obtained in injured and control cultures. In a subsequent application, we study the evolution of latency patterns over several days during the lifetime of a dissociated hippocampal culture.