The neural processes used to extract perceptual features of vocal calls, and subsequently to re-integrate those features to form a coherent auditory object, are poorly understood. In this thesis, extracellular recordings were carried out in order to investigate how the temporal envelope, pitch, timbre and spatial location of communication sounds are represented by neurons in two core and three belt areas of ferret (Mustela putorius furo) auditory cortex. Potential neural underpinnings of auditory perception were tested using neurometric analysis to relate the reliability of neural responses to the performance of ferret and human listeners on psychophysical tasks. I found that human listeners' discrimination of the temporal envelopes of vocalization sounds matched the best neurometrics calculated from the temporal spiking patterns of ferret cortical neurons. Neurometric scores based on the spike rates of cortical neurons accounted for ferrets' discrimination of the pitch of artificial vowels. I show that most auditory cortical neurons are modulated by a number of stimulus features, rather than being tuned to only one feature. Neurons in the core auditory cortical fields often respond uniquely to particular combinations of pitch and timbre features, while those in belt regions respond more linearly to feature combinations. Subtle differences in the sensitivity of neurons to pitch, timbre and azimuthal cues were found across cortical areas and depths. These results suggest that auditory cortical neurons provide widely distributed representations of vocalizations, and a single neuron can often use combinations of spike rate and temporal spiking responses to encode multiple sound features.

Este trabalho descreve o desenvolvimento de um sistema de simulação de circuitos neuronais, com interface de utilização amigável e arquitetura baseada em web. O sistema é direcionado ao estudo de redes de neurônios da medula espinhal, responsáveis pelo controle motor, sujeitas à ativação por vias superiores e periféricas ou por estímulos elétricos. Sua utilidade está relacionada à criação de hipóteses ou teorias sobre o processamento neuronal realizado no caso são ou patológico, a atividades como a interpretação de resultados de experimentos eletrofisiológicos realizados em humanos e no direcionamento e validação de procedimentos experimentais. Para os propósitos deste projeto, a simulação computacional é o recurso mais indicado a se utilizar, considerando o grande número de variáveis envolvidas e o caráter não-linear dos elementos constituintes. As simulações devem retratar de maneira fidedigna as principais propriedades que caracterizam os núcleos neuronais a se estudar. Essas propriedades estão associadas ao recrutamento de unidades motoras, às relações de entrada-saída dos conjuntos neuronais, à influência das vias aferentes sobre os motoneurônios, ao papel da inibição recorrente e da inibição recíproca, à geração de força e do sinal eletromiográfico, entre outros. A simulação do reflexo H, que é uma técnica muito importante utilizada em estudos neurofisiológicos, está presente neste trabalho. Pretende-se que o sistema de simulação aqui proposto seja uma ferramenta útil para pesquisa e ensino da neurofisiologia do controle motor, provendo subsídios que levem a um melhor entendimento dos circuitos neuronais modelados. / This work describes the development of a simulation system of neuronal circuitry, having a user-friendly interface and based on web architecture. The system is intended for studying spinal cord neuronal networks responsible for muscle control, subjected to descending drive or electrical stimulation. It is potentially useful in many activities, such as the interpretation of electrophysiological experiments conducted with humans, the proposition of hypotheses or theories on neuronal processing. Computer simulation is the most indicated approach to attain the objectives of this project because of the huge number of variables and the non-linear characteristics of the constituting elements. The simulations should mimic in a faithful way the main properties related to the modeled neuronal nuclei. These properties are associated with: i) motor-unit recruitment, ii) neuronal nuclei input-output relations, iii) afferent tract influence on motoneurons, iv) effects of recurrent inhibition and reciprocal inhibition, v) generation of force and electromyogram, and others. The generation of the H-reflex by the Ia-motoneuron pool system, which is an important tool in human neurophysiology, is included in the simulation system. The biological reality obtained with the present simulator and its web-based implementation make it a powerful tool for researchers in neurophysiology.

Em modelos de redes corticais com neuronios pulsantes, os mecanismos responsaveis pela emergencia e impacto de flutuacoes de atividade neuronal ainda nao estao completamente entendidos. Neste trabalho, modelos computacionais de redes corticais foram utilizados para investigar como flutuacoes ritmicas e nao-ritmicas surgem e suas possiveis consequencias. Foram estudadas redes com dois tipos de topologia: aleatoria e hierarquica modular, esta ultima inspirada em evidencias experimentais para a arquitetura cortical. Foram utilizados tres diferentes modelos simplificados de neuronios: integra-e-dispara, Izhikevich e integra-e-dispara exponencial com adaptacao. Primeiramente, estudou-se a ocorrencia de atividade auto-sustentada em redes hierarquicas modulares compostas por populacoes de neuronios de classes eletrofisiologicas distintas. Nesses modelos, os padroes de atividade auto-sustentada de longa duracao sao oscilatorios e seu tempo de vida depende do nivel hierarquico e da mistura de neuronios na rede. Em seguida, estudou-se o efeito da introducao de ruido sinaptico em modelos de redes aleatorias. Observou-se o aparecimento de alternancia intermitente entre atividade ritmica e nao-ritmica com caracteristicas similares a estados corticais sincronos e assincronos, respectivamente. Desenvolveu-se a extensao de uma abordagem reducionista para redes neuronais homogeneas, em que um esquema iterativo auto-consistente e usado para que um unico neuronio gere trens de disparo com propriedades estatisticas de segunda ordem similares as de uma rede, para o caso de redes neuronais heterogeneas. Mostrou-se que essa abordagem captura situacoes em que flutuacoes de atividade lentas emergem. Finalmente, utilizou-se o esquema reducionista e ferramentas de teoria de informacao para estudar a emergencia de flutuacoes de atividade lentas e sua propagacao em redes hierarquicas modulares. Os resultados mostram que a propagacao de informacao pela rede depende do numero de modulos, sugerindo que ha um nivel hierarquico otimo para a propagacao de informacao. Os estudos feitos contribuem para aprofundar o entendimento da relacao entre estrutura e composicao neuronal em modelos de redes corticais e indicam mecanismos de emergencia e manutencao de flutuacoes de atividade nessas redes / In cortical network models with spiking neurons, the mechanisms responsible for the emergence and impact of neuronal activity fluctuations are not yet completely understood. In this work, computational models of cortical networks were used to investigate how rhythmic and non-rhythmic fluctuations arise and their possible consequences. Networks with two types of topology were studied: random and hierarchical modular, this latter inspired on experimental evidence about cortical architecture. Three different simplified spiking neuron models were used: integrate-and-fire, Izhikevich, and integrate-and-fire with adaptation. Initially, the types of self-sustained activity patterns that emerge in hierarchical modular networks with mixtures of electrophysiological neuronal classes were studied. In these models, the long-duration self-sustained activity patterns are oscillatory and their lifetime depend on the hierarchical level of the network and its neuronal composition. Next, the effect of the introduction of synaptic noise in random networks was studied. These networks displayed intermittent alternations between rhythmic and non-rhythmic activity patterns with characteristics similar to synchronous and asynchronous cortical states, respectively. A reductionist approach for homogeneous neuronal networks, in which an iterative self-consistent scheme is used so that a single neuron spike train generates second-order statistical properties similar to the ones of a network, was extended to heterogeneous networks. It was shown that this reductionist scheme captures situations in which slow activity fluctuations emerge. Finally, the reductionist scheme and information theoretical tools were used to study the emergence of slow activity fluctuations and their propagation through hierarchical modular networks. The results show that the information propagation in the network depends on the number of modules, suggesting an optimal hierarchical level for information propagation. The studies done contribute to deepen the understanding of the relationship between structure and neuronal composition in cortical network models, and point to mechanisms of emergence and maintenance of activity fluctuations in these networks

O estudo do controle neurofisiológico do movimento tem sido realizado sob várias perspectivas. Experimentos com seres humanos são realizados durante a execução de uma dada tarefa motora e, frequentemente, mediante a aplicação de estímulos externos (elétrico, magnético ou mecânico) ao sistema neuromuscular. Estes experimentos fornecem uma grande quantidade de dados referentes ao funcionamento das redes neuronais e dos atuadores biomecânicos envolvidos nos procedimentos. Entretanto, alguns achados experimentais permanecem incompreensíveis, requerendo a utilização de outros recursos para elucidar quais mecanismos estão por trás dos resultados. Neste sentido, a modelagem matemática e a simulação computacional servem como parte importante destas ferramentas que são imprescindíveis para uma melhor compreensão dos mecanismos neurofisiológicos e biomecânicos por trás do controle do movimento. A presente tese de doutorado teve como objetivo prover um modelo neuromusculoesquelético biologicamente plausível capaz de investigar diferentes mecanismos responsáveis pelo controle do torque gerado na articulação do tornozelo. Este modelo teve como base um modelo neuromuscular previamente proposto, porém, que não incorporava uma série de elementos fundamentais para um estudo mais amplo do sistema motor. O novo modelo proposto contempla modelos de motoneurônios com dendritos ativos, proprioceptores musculares responsáveis pelas vias reflexas de curta e média latência, modelos que representam as características viscoelásticas dos músculos e um modelo biomecânico do ser humano durante a postura ereta quieta. O modelo foi aplicado a diferentes problemas relacionados ao funcionamento do sistema neuromusculoesquelético, que são tipicamente explorados por experimentos com seres humanos, e forneceu bases teóricas importantes para estes achados. / The neurophysiological control of movement has been studied from several standpoints. Human experiments are performed during the execution of a given motor task and, frequently, by applying an external stimulation (electrical, magnetic, or mechanical) to the neuromuscular system. These experiments provide a large amount of data concerning the functioning of the neuronal networks and biomechanical actuators involved in the procedures. Nonetheless, some experimental findings remain puzzling, so that other available resources should be used to clarify what mechanisms are behind these results. In this vein, the mathematical modeling and computer simulations are invaluable tools that may be used to better understand the neurophysiological and biomechanical mechanisms underlying the motor control. The present PhD thesis aimed at providing a biologically plausible neuromusculoskeletal model that was used to study different mechanisms involved in the control of the ankle joint torque. This model was based on a previous neuromuscular model, which did not employ several elements that are fundamental to a comprehensive evaluation of the motor system. The novel proposed model encompasses motor neuron models with active dendrites, muscle proprioceptors responsible for the short- and medium-latency reflex pathways, muscle models with the main viscoelastic features, and a biomechanical model of the human body during upright stance. It was applied to a series of problems frequently related to the functioning of the neuromusculoskeletal system and its main outcomes provided important theoretical bases for a set of experimental findings.

O estudo do controle neurofisiológico do movimento tem sido realizado sob várias perspectivas. Experimentos com seres humanos são realizados durante a execução de uma dada tarefa motora e, frequentemente, mediante a aplicação de estímulos externos (elétrico, magnético ou mecânico) ao sistema neuromuscular. Estes experimentos fornecem uma grande quantidade de dados referentes ao funcionamento das redes neuronais e dos atuadores biomecânicos envolvidos nos procedimentos. Entretanto, alguns achados experimentais permanecem incompreensíveis, requerendo a utilização de outros recursos para elucidar quais mecanismos estão por trás dos resultados. Neste sentido, a modelagem matemática e a simulação computacional servem como parte importante destas ferramentas que são imprescindíveis para uma melhor compreensão dos mecanismos neurofisiológicos e biomecânicos por trás do controle do movimento. A presente tese de doutorado teve como objetivo prover um modelo neuromusculoesquelético biologicamente plausível capaz de investigar diferentes mecanismos responsáveis pelo controle do torque gerado na articulação do tornozelo. Este modelo teve como base um modelo neuromuscular previamente proposto, porém, que não incorporava uma série de elementos fundamentais para um estudo mais amplo do sistema motor. O novo modelo proposto contempla modelos de motoneurônios com dendritos ativos, proprioceptores musculares responsáveis pelas vias reflexas de curta e média latência, modelos que representam as características viscoelásticas dos músculos e um modelo biomecânico do ser humano durante a postura ereta quieta. O modelo foi aplicado a diferentes problemas relacionados ao funcionamento do sistema neuromusculoesquelético, que são tipicamente explorados por experimentos com seres humanos, e forneceu bases teóricas importantes para estes achados. / The neurophysiological control of movement has been studied from several standpoints. Human experiments are performed during the execution of a given motor task and, frequently, by applying an external stimulation (electrical, magnetic, or mechanical) to the neuromuscular system. These experiments provide a large amount of data concerning the functioning of the neuronal networks and biomechanical actuators involved in the procedures. Nonetheless, some experimental findings remain puzzling, so that other available resources should be used to clarify what mechanisms are behind these results. In this vein, the mathematical modeling and computer simulations are invaluable tools that may be used to better understand the neurophysiological and biomechanical mechanisms underlying the motor control. The present PhD thesis aimed at providing a biologically plausible neuromusculoskeletal model that was used to study different mechanisms involved in the control of the ankle joint torque. This model was based on a previous neuromuscular model, which did not employ several elements that are fundamental to a comprehensive evaluation of the motor system. The novel proposed model encompasses motor neuron models with active dendrites, muscle proprioceptors responsible for the short- and medium-latency reflex pathways, muscle models with the main viscoelastic features, and a biomechanical model of the human body during upright stance. It was applied to a series of problems frequently related to the functioning of the neuromusculoskeletal system and its main outcomes provided important theoretical bases for a set of experimental findings.

The studies in this thesis explored how pain and its relief are represented in the human brain. Pain and relief are important survival signals that motivate escape from danger and search for safety, however, they are often evaluated by subjective descriptions only. Studying how humans learn and adapt to pain and relief allows objective investigation of the information processing and neural circuitry underlying these internal experiences. My research set out to use computational learning models to provide mechanistic explanations for the behavioural and functional neuroimaging data collected in pain/relief learning experiments with independent groups of healthy human participants. With a Pavlovian acute pain conditioning task in Experiment 1, I found that 'associability' (a form of uncertainty signal) had a crucial role in controlling the learning rates of different conditioned responses, and can be used to anatomically dissociate underlying neural systems. Experiment 2 focused on relief learning of terminating a tonic pain stimulus, in which the priority for relief-seeking is in conflict with the general suppression of cognition and attention. I showed that associability during active learning not only controls the relief learning rate, but also correlates with endogenously modulated (reduced) ongoing pain. This finding was confirmed in Experiment 3 using an independent active relief learning paradigm in a complex dynamic environment. Critically, both experiments showed that associability was correlated with responses in the pregenual anterior cingulate cortex (pgACC), a brain region previously implicated in aspects of endogenous pain control related to attention and controllability. This provided a potential computational account of an information-sensitive endogenous analgesic mechanism. In Experiment 4, I explored the implications of endogenous controllability for technology-based pain therapeutics. I designed an adaptive closed-loop system that learned to control pain stimulation using decoded real-time pain representations from the brain. Subjects were shown to actively enhance the discriminability of pain only in the pgACC, and uncertainty during learning again correlated with endogenously modulated pain and were associated with pgACC responses. Together, these studies (i) show the importance of uncertainty in controlling learning during both acute and tonic pain, (ii) describe how uncertainty also flexibly modulates pain to maximise the impact of learning, (iii) illustrate a central role for the pgACC in this process, and (iv) reveal the implications for future technology-based therapeutic systems.

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.

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.

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.

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.