O encéfalo, através de complexa atividade elétrica, é capaz de processar diversos tipos de informação, que são reconhecidos, memorizados e recuperados. A base do processamento é dada pela atividade de neurônios, que se comunicam principalmente através de eventos discretos no tempo: os potenciais de ação. Os disparos desses potenciais de ação podem ser observados por técnicas experimentais; por exemplo, é possível medir os instantes dos disparos dos potenciais de ação de centenas de neurônios em camundongos vivos. No entanto, as intensidades das conexões entre esses neurônios não são totalmente acessíveis, o que, além de outros fatores, impossibilita um entendimento mais completo do funcionamento da rede neural. Desse modo, a neurociência computacional tem papel importante para o entendimento dos processos envolvidos no encéfalo, em vários níveis de detalhamento. Dentro da área da neurociência computacional, o presente trabalho aborda a aquisição e recuperação de memórias dadas por padrões espaciais, onde o espaço é definido pelos neurônios da rede simulada. Primeiro utilizamos o conceito da regra de Hebb para construir redes de neurônios com conexões previamente definidas por esses padrões espaciais. Se as memórias são armazenadas nas conexões entre os neurônios, então a inclusão de um período de aprendizado torna necessária a implementação de plasticidade nos pesos sinápticos. As regras de modificação sináptica que permitem memorização (Hebbianas) geralmente causam instabilidades na atividade dos neurônios. Com isso desenvolvemos regras de plasticidade homeostática capazes de estabilizar a atividade basal de redes de neurônios. Finalizamos com o estudo analítico e numérico de regras de plasticidade sináptica que permitam o aprendizado não-supervisionado por elevação da taxa de disparos de potenciais de ação de neurônios. Mostramos que, com uma regra de aprendizado baseada em evidências experimentais, a recuperação de padrões memorizados é possível, com ativação supervisionada ou espontânea. / The brain, through complex electrical activity, is able to process different types of information, which are encoded, stored and retrieved. The processing is based on the activity of neurons that communicate primarily by discrete events in time: the action potentials. These action potentials can be observed via experimental techniques; for example, it is possible to measure the moment of action potentials (spikes) of hundreds of neurons in living mice. However, the strength of the connections among these neurons is not fully accessible, which, among other factors, preclude a more complete understanding of the neural network. Thus, computational neuroscience has an important role in understanding the processes involved in the brain, at various levels of detail. Within the field of computational neuroscience, this work presents a study on the acquisition and retrieval of memories given by spatial patterns, where space is defined by the neurons of the simulated network. First we use Hebb’s rule to build up networks of spiking neurons with static connections chosen from these spatial patterns. If memories are stored in the connections between neurons, then synaptic weights should be plastic so that learning is possible. Synaptic plasticity rules that allow memory formation (Hebbian) usually introduce instabilities on the neurons’ activity. Therefore, we developed homeostatic plasticity rules that stabilize baseline activity regimes in neural networks of spiking neurons. This thesis ends with analytical and numerical studies regarding plasticity rules that allow unsupervised learning by increasing the activity of specific neurons. We show that, with a plasticity rule based on experimental evidences, retrieval of learned patterns is possible, either with supervised or spontaneous recalling.

O encéfalo, através de complexa atividade elétrica, é capaz de processar diversos tipos de informação, que são reconhecidos, memorizados e recuperados. A base do processamento é dada pela atividade de neurônios, que se comunicam principalmente através de eventos discretos no tempo: os potenciais de ação. Os disparos desses potenciais de ação podem ser observados por técnicas experimentais; por exemplo, é possível medir os instantes dos disparos dos potenciais de ação de centenas de neurônios em camundongos vivos. No entanto, as intensidades das conexões entre esses neurônios não são totalmente acessíveis, o que, além de outros fatores, impossibilita um entendimento mais completo do funcionamento da rede neural. Desse modo, a neurociência computacional tem papel importante para o entendimento dos processos envolvidos no encéfalo, em vários níveis de detalhamento. Dentro da área da neurociência computacional, o presente trabalho aborda a aquisição e recuperação de memórias dadas por padrões espaciais, onde o espaço é definido pelos neurônios da rede simulada. Primeiro utilizamos o conceito da regra de Hebb para construir redes de neurônios com conexões previamente definidas por esses padrões espaciais. Se as memórias são armazenadas nas conexões entre os neurônios, então a inclusão de um período de aprendizado torna necessária a implementação de plasticidade nos pesos sinápticos. As regras de modificação sináptica que permitem memorização (Hebbianas) geralmente causam instabilidades na atividade dos neurônios. Com isso desenvolvemos regras de plasticidade homeostática capazes de estabilizar a atividade basal de redes de neurônios. Finalizamos com o estudo analítico e numérico de regras de plasticidade sináptica que permitam o aprendizado não-supervisionado por elevação da taxa de disparos de potenciais de ação de neurônios. Mostramos que, com uma regra de aprendizado baseada em evidências experimentais, a recuperação de padrões memorizados é possível, com ativação supervisionada ou espontânea. / The brain, through complex electrical activity, is able to process different types of information, which are encoded, stored and retrieved. The processing is based on the activity of neurons that communicate primarily by discrete events in time: the action potentials. These action potentials can be observed via experimental techniques; for example, it is possible to measure the moment of action potentials (spikes) of hundreds of neurons in living mice. However, the strength of the connections among these neurons is not fully accessible, which, among other factors, preclude a more complete understanding of the neural network. Thus, computational neuroscience has an important role in understanding the processes involved in the brain, at various levels of detail. Within the field of computational neuroscience, this work presents a study on the acquisition and retrieval of memories given by spatial patterns, where space is defined by the neurons of the simulated network. First we use Hebb’s rule to build up networks of spiking neurons with static connections chosen from these spatial patterns. If memories are stored in the connections between neurons, then synaptic weights should be plastic so that learning is possible. Synaptic plasticity rules that allow memory formation (Hebbian) usually introduce instabilities on the neurons’ activity. Therefore, we developed homeostatic plasticity rules that stabilize baseline activity regimes in neural networks of spiking neurons. This thesis ends with analytical and numerical studies regarding plasticity rules that allow unsupervised learning by increasing the activity of specific neurons. We show that, with a plasticity rule based on experimental evidences, retrieval of learned patterns is possible, either with supervised or spontaneous recalling.

O entendimento dos mecanismos de representação e processamento de odores pelo sistema olfatório é uma das questões centrais da neurociência moderna. Os odores são codificados pela circuitaria interna do bulbo olfatório em padrões espaço-temporais refletidos pela atividade de suas células de saída, as células mitrais e tufosas, que transmitem os resultados das computações dessa estrutura inicial de processamento a regiões corticais superiores. A arquitetura das conexões existentes no bulbo olfatório apresenta inibição lateral em duas camadas diferentes de sua estrutura laminar, intermediadas por dois tipos distintos de interneurônios. Na camada glomerular, mais externa, a inibição lateral é mediada pelas células periglomerulares e na camada plexiforme externa, mais interna, a inibição lateral é mediada pelas células granulares. O papel desses dois níveis distintos de inibição lateral e os mecanismos segundo os quais eles atuam moldando os padrões espaço-temporais de resposta do bulbo olfatório a odores diferentes são ainda pouco conhecidos. O objetivo deste trabalho foi construir um modelo de rede neural biologicamente plausível do bulbo olfatório para investigar como dois tipos diferentes de interneurônios, atuando em estágios distintos de processamento, podem contribuir para a discriminação de odores e a coordenação dos padrões de disparo das células mitrais. O modelo de rede construído, com representação de odores pela atividade das células mitrais e baseado nas interações recíprocas entre essas células e os interneurônios inibitórios, mostrou que a inibição gerada pelas células periglomerulares pode melhorar o contraste entre odores similares, facilitando a discriminação de odores, enquanto que a inibição das células granulares atua no refinamento da resposta de saída da informação olfatória. / The understanding of odor representation and processing mechanisms by the olfactory system is one of the central questions of modern neuroscience. Odors are encoded by the olfactory bulb circuitry in terms of spatiotemporal spiking patterns. These are reflected in the activity of the mitral cells, which are the output cells of the olfactory bulb that transmit the information processed in this early structure to higher cortical regions. The architecture of the olfactory bulb connections presents lateral inhibition at two different layers of its laminar structure, mediated by two distinct types of interneurons. In the glomerular layer, lateral inhibition is mediated by periglomerular cells. In the external plexiform layer, lateral inhibition is mediated by granule cells. The role of these two different lateral inhibition levels and the mechanisms whereby they shape the spatial and temporal patterns of the olfactory bulb response to different odors is not well known. The aim of this work was to build a biologically plausible neural network model of the olfactory bulb to investigate how two different types of interneurons, acting at different processing stages, could contribute to odor discrimination and the coordination of the mitral cells spiking patterns. The results of simulations of the network model shown that the inhibition generated by periglomerular cells can provide contrast enhancement and odors discrimination, while the granule cell inhibition can refine the output response of the olfactory information.

O encéfalo, através de complexa atividade elétrica, é capaz de processar diversos tipos de informação, que são reconhecidos, memorizados e recuperados. A base do processamento é dada pela atividade de neurônios, que se comunicam principalmente através de eventos discretos no tempo: os potenciais de ação. Os disparos desses potenciais de ação podem ser observados por técnicas experimentais; por exemplo, é possível medir os instantes dos disparos dos potenciais de ação de centenas de neurônios em camundongos vivos. No entanto, as intensidades das conexões entre esses neurônios não são totalmente acessíveis, o que, além de outros fatores, impossibilita um entendimento mais completo do funcionamento da rede neural. Desse modo, a neurociência computacional tem papel importante para o entendimento dos processos envolvidos no encéfalo, em vários níveis de detalhamento. Dentro da área da neurociência computacional, o presente trabalho aborda a aquisição e recuperação de memórias dadas por padrões espaciais, onde o espaço é definido pelos neurônios da rede simulada. Primeiro utilizamos o conceito da regra de Hebb para construir redes de neurônios com conexões previamente definidas por esses padrões espaciais. Se as memórias são armazenadas nas conexões entre os neurônios, então a inclusão de um período de aprendizado torna necessária a implementação de plasticidade nos pesos sinápticos. As regras de modificação sináptica que permitem memorização (Hebbianas) geralmente causam instabilidades na atividade dos neurônios. Com isso desenvolvemos regras de plasticidade homeostática capazes de estabilizar a atividade basal de redes de neurônios. Finalizamos com o estudo analítico e numérico de regras de plasticidade sináptica que permitam o aprendizado não-supervisionado por elevação da taxa de disparos de potenciais de ação de neurônios. Mostramos que, com uma regra de aprendizado baseada em evidências experimentais, a recuperação de padrões memorizados é possível, com ativação supervisionada ou espontânea. / The brain, through complex electrical activity, is able to process different types of information, which are encoded, stored and retrieved. The processing is based on the activity of neurons that communicate primarily by discrete events in time: the action potentials. These action potentials can be observed via experimental techniques; for example, it is possible to measure the moment of action potentials (spikes) of hundreds of neurons in living mice. However, the strength of the connections among these neurons is not fully accessible, which, among other factors, preclude a more complete understanding of the neural network. Thus, computational neuroscience has an important role in understanding the processes involved in the brain, at various levels of detail. Within the field of computational neuroscience, this work presents a study on the acquisition and retrieval of memories given by spatial patterns, where space is defined by the neurons of the simulated network. First we use Hebb’s rule to build up networks of spiking neurons with static connections chosen from these spatial patterns. If memories are stored in the connections between neurons, then synaptic weights should be plastic so that learning is possible. Synaptic plasticity rules that allow memory formation (Hebbian) usually introduce instabilities on the neurons’ activity. Therefore, we developed homeostatic plasticity rules that stabilize baseline activity regimes in neural networks of spiking neurons. This thesis ends with analytical and numerical studies regarding plasticity rules that allow unsupervised learning by increasing the activity of specific neurons. We show that, with a plasticity rule based on experimental evidences, retrieval of learned patterns is possible, either with supervised or spontaneous recalling.

A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação. / Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.

Dans cette thèse, nous étudions le vaste domaine des neurosciences à travers des outils théoriques, numériques et expérimentaux. Nous étudions comment les modèles à taux de décharge peuvent être utilisés pour capturer différents phénomènes observés dans le cerveau. Nous étudions les régimes dynamiques des réseaux couplés de neurones excitateurs (E) et inhibiteurs (I): Nous utilisons une description fournie par un modèle à taux de décharge et la comparons avec les simulations numériques des réseaux de neurones à potentiel d'action décrits par le modèle EIF. Nous nous concentrons sur le régime où le réseau EI présente des oscillations, puis nous couplons deux de ces réseaux oscillants pour étudier la dynamique résultante. La description des différents régimes pour le cas de deux populations est utile pour comprendre la synchronisation d'une chaine de modules E-I et la propagation d'ondes observées dans le cerveau. Nous examinons également les modèles à taux de décharge pour décrire l'adaptation sensorielle: Nous proposons un modèle de ce type pour décrire l'illusion du mouvement consécutif («motion after effect», (MAE)) dans la larve du poisson zèbre. Nous comparons le modèle à taux de décharge avec des données neuronales et comportementales nouvelles. / In this thesis, we investigate the vast field of neuroscience through theoretical, numerical and experimental tools. We study how rate models can be used to capture various phenomena observed in the brain. We study the dynamical regimes of coupled networks of excitatory (E) and inhibitory neurons (I) using a rate model description and compare with numerical simulations of networks of neurons described by the EIF model. We focus on the regime where the EI network exhibits oscillations and then couple two of these oscillating networks to study the resulting dynamics. The description of the different regimes for the case of two populations is helpful to understand the synchronization of a chain of E-I modules and propagation of waves observed in the brain. We also look at rate models of sensory adaptation. We propose one such model to describe the illusion of motion after effect in the zebrafish larva. We compare this rate model with newly obtained behavioural and neuronal data in the zebrafish larva.

L'homme est capable d'utiliser des stratégies ou règles concurrentes selon les contraintes environnementales. Nous étudions un modèle plausible pour une tâche nécessitant l'apprentissage de plusieurs règles associant des stimuli visuels à des réponses motrices. Deux réseaux de populations neurales à sélectivité mixte interagissent. Le réseau décisionnel apprend les associations stimulus-réponse une à une, mais ne peut gérer qu'une règle à la fois. Son activité modifie la plasticité synaptique du second réseau qui apprend les statistiques d'évènements sur une échelle de temps plus longue. Lorsque des motifs entre les associations stimulus-réponse sont détectés, un biais d'inférence vers le réseau décisionnel guide le comportement futur. Nous montrons que le mécanisme de Hebb non-supervisé dans le second réseau est suffisant pour l'implémentation des règles. Leur récupération dans le réseau de décision améliore la performance. Le modèle prédit des changements comportementaux en fonction de la séquence des réponses précédentes, dont les effets sur la performance peuvent être positifs ou négatifs. Les prédictions sont confirmées par les données, et permettent d'identifier les sujets ayant appris la structure de la tâche. Le signal d'inférence corrèle avec l'activité BOLD dans le réseau fronto-pariétal. Au sein de ce réseau, les n¿uds préfrontaux dorsomédial et dorsolatéral sont préférentiellement recrutés lorsque les règles sont récurrentes: l'activité dans ces régions pourrait biaiser les circuits de décision lorsqu'une règle est récupérée. Ces résultats montrent que le mécanisme de Hebb peut expliquer l'apprentissage de comportements complexes en contrôle cognitif. / Depending on environmental demands, humans performing in a given task are able to exploit multiple concurrent strategies, for which the mental representations are called task-sets. We examine a candidate model for a specific human experiment, where several stimulus-response mappings, or task-sets, need to be learned and monitored. The model is composed of two interacting networks of mixed-selective neural populations. The decision network learns stimulus-response associations, but cannot learn more than one task-set. Its activity drives synaptic plasticity in a second network that learns event statistics on a longer timescale. When patterns in stimulus-response associations are detected, an inference bias to the decision network guides successive behavior. We show that a simple unsupervised Hebbian mechanism in the second network is sufficient to learn an implementation of task-sets. Their retrieval in the decision network improves performance. The model predicts abrupt changes in behavior depending on the precise statistics of previous responses, corresponding to positive (task-set retrieval) or negative effects on performance. The predictions are borne out by the data, and enable to identify subjects who have learned the task structure. The inference signal correlates with BOLD activity in the fronto-parietal network. Within this network, dorsomedial and dorsolateral prefrontal nodes are preferentially recruited when task-sets are recurrent: activity in these regions may provide a bias to decision circuits when a task-set is retrieved. These results show that Hebbian mechanisms and temporal contiguity may parsimoniously explain the learning of rule-guided behavior.

A stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear. Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found.

Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations, even if the average synchrony level is the same. In this thesis, we use computational neuroscience methods to investigate the effects of (i) spike-timing dependent plasticity (STDP) and (ii) noise on the temporal patterns of synchronization in a simple model. The model is composed of two conductance-based neurons connected via excitatory unidirectional synapses. In (i) these excitatory synapses are made plastic, in (ii) two different types of noise implementation to model the stochasticity of membrane ion channels is considered. The plasticity results are taken from our recently published article, while the noise results are currently being compiled into a manuscript.<br><br>The dynamics of this network is subjected to the time-series analysis methods used in prior experimental studies. We provide numerical evidence that both STDP and channel noise can alter the synchronized dynamics in the network in several ways. This depends on the time scale that plasticity acts on and the intensity of the noise. However, in general, the action of STDP and noise in the simple network considered here is to promote dynamics with short desynchronizations (i.e. dynamics reminiscent of that observed in experimental studies) over dynamics with longer desynchronizations.

<p>It was only in the last decade that Magnetic Resonance Imaging (MRI) technologies have achieved high-quality levels that enabled comprehensive assessments of individual human brain structure and functions. One of the most important advancements put forth by Thomas Yeo and colleagues in 2011 was the intrinsic functional connectivity MRI (fcMRI) networks which are highly reproducible and feature consistently across different individual brains. This dissertation aims to unravel different characteristics of human brain fcMRI networks, separately through network morphospace and collectively through stochastic block models.</p><p><br></p><p>The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE). We use this framework to quantify the Network Configural Breadth across different tasks. Network configural breadth is shown to significantly predict behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence.</p><p><br></p><p>To properly estimate and assess whole-brain functional connectomes (FCs) is among one of the most challenging tasks in computational neuroscience. Among the steps in constructing large-scale brain networks, thresholding of statistically spurious edge(s) in FCs is the most critical. State-of-the-art thresholding methods are largely ad hoc. Meanwhile, a dominant proportion of the brain connectomics research relies heavily on using a priori set of highly-reproducible human brain functional sub-circuits (functional networks (FNs)) without properly considering whether a given FN is information-theoretically relevant with respect to a given FC. Leveraging recent theoretical developments in Stochastic block model (SBM), we first formally defined and subsequently quantified the level of information-theoretical prominence of a priori set of FNs across different subjects and fMRI task conditions for any given input FC. The main contribution of this work is to provide an automated thresholding method of individuals’ FCs based on prior knowledge of human brain functional sub-circuitry.</p>