Dans cette thèse, nous avons étudié le codage prédictif and sa relation avec la perception et les oscillations. Nous avons, dans l'introduction, fait une revue des connaissances sur les neurones et le néocortex et un état de l'art du codage prédictif. Dans les chapitres principaux, nous avons tout d'abord, proposé l'idée, au travers d'une étude théorique, que la temporalité de la décharge crée une inhibition sélective dans les réseaux excitateurs non-sélectifs rétroactifs. Ensuite, nous avons montré les effets perceptuels du codage prédictif: la perception de la forme améliore la perception du contraste. Enfin, nous avons montré que le codage prédictif peut utiliser des oscillations dans différentes bandes de fréquences pour transmettre les informations en avant et en rétroaction. Cette thèse a fourni un mécanisme neuronal viable et innovant pour le codage prédictif soutenu par des données empiriques démontrant des prédictions rétroactives excitatrices et une relation forte entre codage prédictif et oscillations. / In this thesis, we investigated predictive coding and its relationship with perception and oscillations. We first reviewed my current understanding about facts of neuron and neocortex and state-of-the-arts of predictive coding in the introduction. In the main chapters, firstly, we proposed the idea that correlated spike times create selective inhibition in a nonselective excitatory feedback network in a theoretical study. Then, we showed the perceptual effect of predictive coding: shape perception enhances perceived contrast. At last, we showed that predictive coding can use oscillations with different frequencies for feedforward and feedback. This thesis provided an innovative and viable neuronal mechanism for predictive coding and empirical evidence for excitatory predictive feedback and the close relationship between the predictive coding and oscillations.

Korrelationskodierung im Hippokampus bildet möglicherweise die neuronale Basis für episodisches Gedächtnis. In dieser Arbeit untersuchen wir zwei Phänomene der Korrelationskodierung: Phasenpräzession und Sequenzwiederholungen. Phasenpräzession bezeichnet die Abnahme der Phase des Aktionspotentials einer Ortszelle relativ zur Theta-Oszillation. Sequenzwiederholung beschreibt die Aktivität von Ortszellen in Ruhephasen; dabei werden vorangegangene Orts- Sequenzen in umgekehrter Reihenfolge wiederholt. Wir untersuchen Phasenpräzession in einzelnen Versuchsdurchläufen. In bisherigen Studien wurden Daten zur Phasenpräzession in vielen Versuchsdurchläufen zusammengelegt. Wir zeigen, dass dies zu einer verzerrten Schätzung von grundlegenden Eigenschaften der Phasenpräzession führen kann. Weiterhin demonstrieren wir eine starke Variabilität der Phasenpräzession zwischen verschiedenen Versuchsdurchläufen. Daher ist Phasenpräzession besser geeignet zeitlich strukturierte Sequenzen zu lernen, als man aufgrund der zusammengelegten Daten vermutet hatte. Desweiteren untersuchen wir die Beziehung von Phasenpräzession in unterschiedlichen Teilen des Hippokampus. Wir zeigen, dass die extrazellulären Theta- Oszillationen in CA3 und CA1 außer Phase sind. Dennoch geschieht Phasenpräzession in beiden Regionen fast gleichzeitig, und CA3 Zellen feuern oft kurz vor CA1 Zellen. Diese zeitliche Beziehung ist im Einklang mit einer Vererbung von Phasenpräzession von CA3 nach CA1. Wir entwickeln ein mechanistisches Modell für Sequenzwiederholungen in umgekehrter Reihenfolge basierend auf Kurzzeitfazilitierung. Mit Hilfe des Tempotrons beweisen wir, dass die entstehenden zeitlichen Muster geeignet sind, um von nachgeschalteten Strukturen ausgelesen zu werden. Das Modell sagt voraus, dass im Gyrus Dentatus synchrone Zellaktivität kurz vor einer Sequenzwiederholung in CA3 zu sehen ist, und es zeigt, dass Sequenzwiederholungen zum Lernen von zeitlichen Mustern genutzt werden können. / Hippocampal correlation coding is a putative neural mechanism underlying episodic memory. Here, we look at two related phenomena: phase precession and reverse replay of sequences. Phase precession refers to the decrease of the firing phase of a place cell with respect to the local theta rhythm during the crossing of the place field. Reverse replay refers to reactivation of previously experienced place field sequences in reverse order during awake resting periods. First, we study properties of phase precession in single trials. Usually, phase precession is studied on the basis of data in which many place field traversals are pooled together. We find that single-trial and pooled-trial phase precession are different with respect to phase-position correlation, phase-time correlation, and phase range. We demonstrate that phase precession exhibits a large trial-to-trial variability and that pooling trials changes basic measures of phase precession. These findings indicate that single trials may be better suited for encoding temporally structured events than is suggested by the pooled data. Second, we examine the coordination of phase precession among subregions of the hippocampus. We find that the local theta rhythms in CA3 and CA1 are almost antiphasic. Still, phase precession in the two regions occurs with only a small phase shift, and CA3 cells tend to fire a few milliseconds before CA1 cells. These results suggest that phase precession in CA1 might be inherited from CA3. Finally, we present a model of reverse replay based on short-term facilitation. The model compresses temporal patterns from a behavioral time scale of seconds to shorter time scales relevant for synaptic plasticity. We demonstrate that the compressed patterns can be learned by the tempotron learning rule. The model provides testable predictions (synchronous activation of dentate gyrus during sharp wave-ripples) and functional interpretations of hippocampal activity (temporal pattern learning).

Cette recherche visait à clarifier les mécanismes de bas niveau impliqués dans la détection de la modulation de fréquence (FM). Les sons naturels véhiculent des modulations d’amplitude et de fréquence saillantes essentielles à la communication. L’analyse des réponses de neurones auditifs du noyau cochléaire montre que les propriétés spectro-temporelles des stimuli de FM de basse cadence sont représentées par deux mécanismes distincts basés sur le verrouillage en phase à l’enveloppe temporelle (ENV) et à la structure temporelle fine (TFS). La contribution relative de chaque mécanisme s’avère très dépendante des paramètres de stimulation (fréquence porteuse, cadence de modulation et profondeur de modulation) mais aussi du type de neurones, chacun étant spécialisé pour un type de représentation ou l'autre. L’existence de ces deux mécanismes de codage neuronal a été confirmée chez les auditeurs humains en utilisant deux paradigmes psychophysiques. Les résultats de ces études démontrent également que le mécanisme de codage de TFS est efficace dans des conditions d'écoute défavorables (e.g. en présence de modulations interférentes). Cependant, le mécanisme de codage de TFS est susceptible de se dégrader avec l'âge et plus encore avec la perte auditive, alors que le mécanisme de codage d’ENV semble relativement épargné. Deux modèles computationnels ont été développés afin d’expliquer les contributions des indices d’ENV et de TFS dans le système auditif normal et malentendant. / This research aimed at clarifying the low-level mechanisms involved in frequency-modulation (FM) detection. Natural sounds convey salient amplitude- and frequency-modulation patterns crucial for communication. Results from single auditory neurons in the cochlear nucleus show that the spectro-temporal properties of low-rate FM stimuli are accurately represented by two distinct mechanisms based on neural phase-locking to temporal envelope (ENV) and temporal fine structure (TFS) cues. The relative contribution of each mechanism was found to be highly dependent on stimulus parameters (carrier frequency, modulation rate and modulation depth) and also on the type of neuron, with clear specializations for one type of representation or the other. The validity of those two neural encoding mechanisms was confirmed for human listeners using two psychophysical paradigms. Results from those studies also demonstrate that the TFS coding mechanism is efficient in adverse listening conditions, like in the presence of interfering modulations. However, the TFS coding mechanism is prone to decline with age and even more with hearing loss, while the ENV coding mechanism seems relatively spared. Two computational models were developed to fully explain the contributions of ENV and TFS cues in the normal and impaired auditory system.

<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>

Le cervelet permet l’apprentissage moteur et la coordination des mouvements fins. Pour ce faire, il intègre les informations sensorielles provenant de l’ensemble du corps ainsi que les commandes motrices émises par d’autres structures du système nerveux central. Les noyaux cérébelleux profonds (DCN) constituent la sortie du cervelet et intègre les informations provenant des cellules de Purkinje (PC), des fibres moussues et des fibres grimpantes. Nous avons étudié les connexions fonctionnelles entres les PC et les DNC in vivo, grâce à une stimulation optogénétique des lobules IV/V du cortex cérébelleux et à l’enregistrement multi unitaire du noyau médian. Nous avons ainsi identifié deux groupes de cellules au sein des DCN, présentant des caractéristiques propres au niveau de leur fréquence de décharge et de la forme des potentiels d’action, en accord avec la dichotomie établie par une précédente étude in vitro permettant de séparer les neurones GABAergiques des autres neurones. Nos résultats suggèrent que les PC contrôlent la sotie du cervelet d’un point de vue temporel. De plus, la ciruiterie interne des DCN conforte ce résultat de part le fait que les cellules GABAergiques ne produisent pas d’effet temporel au travers de l’inhibition locale. / The cerebellum integrates motor commands with somatosensory, vestibular, visual and auditory information for motor learning and coordination functions. The deep cerebellar nuclei (DCN) generates the final output by processing inputs from Purkinje cells (PC), mossy and climbing fibers. We investigated the properties of PC connections to DCN cells using optogenetic stimulation in L7-ChR2 mice with in vivo multi electrode extracellular recordings in lobule IV/V of the cerebellar cortex and in the medial nuclei. DCN cells discharged phase locked to local field potentials in the beta, gamma and high frequency bands. We identified two groups of DCN cells with significant differences in action potential waveforms and firing rates, matching previously discriminated in vitro properties of GABAergic and non-GABAergic cells. PCs inhibited the two group of cells gradually (rate coding), however spike times were controlled for only non-GABAergic cells. Our results suggest that PC inputs temporally control the output of cerebellum and the internal DCN circuitry supports this phenomenon since GABAergic cells do not induce a temporal effect through local inhibition.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.

<pre><p>Difficulty understanding speech in background noise is the most common hearing complaint. Elucidating the neurophysiological mechanisms underlying speech intelligibility in everyday environments with multiple sound sources and distortions is hence important for any technology that aims to improve real-world listening. Using a combination of behavioral, electroencephalography (EEG), and computational modeling experiments, this dissertation provides insight into how the brain analyzes such complex scenes, and what roles different acoustic cues play in facilitating this process and in conveying phonetic content. Experiment #1 showed that brain oscillations selectively track the temporal envelopes (i.e., modulations) of attended speech in a mixture of competing talkers, and that the strength and pattern of this attention effect differs between individuals. Experiment #2 showed that the fidelity of neural tracking of attended-speech envelopes is strongly shaped by the modulations in interfering sounds as well as the temporal fine structure (TFS) conveyed by the cochlea, and predicts speech intelligibility in diverse listening environments. Results from Experiments #1 and #2 support the theory that temporal coherence of sound elements across envelopes and/or TFS shapes scene analysis and speech intelligibility. Experiment #3 tested this theory further by measuring and computationally modeling consonant categorization behavior in a range of background noises and distortions. We found that a physiologically plausible model that incorporated temporal-coherence effects predicted consonant confusions better than conventional speech-intelligibility models, providing independent evidence that temporal coherence influences scene analysis. Finally, results from Experiment #3 also showed that TFS is used to extract speech content (voicing) for consonant categorization even when intact envelope cues are available. Together, the novel insights provided by our results can guide future models of speech intelligibility and scene analysis, clinical diagnostics, improved assistive listening devices, and other audio technologies.</p></pre>