In this thesis, we study the neurocomputation by implementing two different neuron models. One is a semi magnetic micro p-n wire that emulates nerve fibres and supports the electrical propagation and regeneration. The other is a silicon neuron based on Hodgkin-Huxley conductance model that can generate spatiotemporal spiking patterns. The former model focuses on the spatial propagation of electrical pulses along a transmission line and presents the thesis that action potentials may be represented by solitary waves. The later model focuses on the dynamical properties such as how the output patterns of the active networks adapt to external stimulus. To demonstrate the dynamical properties of spiking networks, we present a central pattern generator (CPG) network with winnerless competition architecture. The CPG consists of three silicon neurons which are connected via reciprocally inhibitory synapses. The network of three neurons was stimulated with current steps possessing different time delays and that the voltage oscillations of the three neurons were recorded as a function of the strengths of inhibitory synaptic interconnections and internal parameters of neurons, such as voltage thresholds, time delays, etc. The architecture of the network is robust and sensitively depends on the stimulus. Stimulus dependent rhythms can be generated by the CPG network. The stimulus-dependent sequential switching between collective modes of oscillations in the network can explain the fundamental contradiction between sensitivity and robustness to external stimulus and the mechanism of pattern memorization. We successfully apply the CPG in modulating the heart rate of animal models (rats). The CPG was stimulated with respiratory signals and generated tri-phasic patterns corresponding to the respiratory cycles. The tri-phasic stimulus from the CPG was used to synchronize the heart rate with respiration. In this way, we artificially induce the respiratory sinus arrhythmia (RSA), which refers to the heart rate fluctuation in synchrony with respiration. RSA is lost in heart failure. Our CPG paves to way to novel medical devices that can provide a therapy for heart failure.

Made available in DSpace on 2014-06-12T23:01:00Z (GMT). No. of bitstreams: 2 arquivo8618_1.pdf: 1604073 bytes, checksum: d9765092ab2a50cd8b1426f73b5ed129 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006 / Este estudo é uma revisão narrativa da literatura sobre os paradigmas computacionais, as modelagens naturais conexionistas e a investigação dos fenômenos psicopatológicos. O objetivo geral foi realizar uma coleta de informações sobre os trabalhos publicados, até então, que contemplassem os modelos de processamento de informações no cérebro humano, a analogia com Redes Neurais Artificiais e a aplicação de métodos investigativos nas psicopatologias. A seleção dos estudos foi baseada principalmente pesquisas em bancos de dados digitais: Medline, Períodos CAPES, MIT Search, Scholar Google e PsychInfo, usando os descritores neural networks, neurocomputation, psychopathology, connectionism, mood disorders, depression, cognition e artificial intelligence, em mecanismos de busca digital. Foram selecionados os estudos considerando os critérios de inclusão a partir dos descritores, o aspecto cronológico, a adequação e pertinência dos estudos e o impacto destes artigos na comunidade científica. A literatura clássica também foi incluída. O estudo buscou estabelecer relações entre as pesquisas que utilizaram ferramentas computacionais, visando a criação de modelos que simularam o funcionamento cognitivo do cérebro humano. Destes modelos, as Redes Neurais Artificiais Conexionistas (RNA) mostraram-se as mais promissoras dentre as demais. Conclui-se que as investigações dos fenômenos psicopatológicos baseadas em modelagem computacional conexionista constituem em uma importante estratégia para compreensão do funcionamento da mente humana e de como se processam as alterações psíquicas

Cotutelle entre l’université de Montréal et d’Aix-Marseille / Cette thèse vise à comprendre les fondements et les fonctions des calculs probabilistes impliqués dans les processus visuels. Nous nous appuyons sur une double stratégie, qui implique le développement de modèles dans le cadre du codage prédictif selon le principe de l'énergie libre. Ces modèles servent à définir des hypothèses claires sur la fonction neuronale, qui sont testées à l'aide d'enregistrements extracellulaires du cortex visuel primaire. Cette région du cerveau est principalement impliquée dans les calculs sur les unités élémentaires des entrées visuelles naturelles, sous la forme de distributions d'orientations. Ces distributions probabilistes, par nature, reposent sur le traitement de la moyenne et de la variance d'une entrée visuelle. Alors que les premières ont fait l'objet d'un examen neurobiologique approfondi, les secondes ont été largement négligées. Cette thèse vise à combler cette lacune. Nous avançons l'idée que la connectivité récurrente intracorticale est parfaitement adaptée au traitement d'une telle variance d'entrées, et nos contributions à cette idée sont multiples. (1) Nous fournissons tout d'abord un examen informatique de la structure d'orientation des images naturelles et des stratégies d'encodage neuronal associées. Un modèle empirique clairsemé montre que le code neuronal optimal pour représenter les images naturelles s'appuie sur la variance de l'orientation pour améliorer l'efficacité, la performance et la résilience. (2) Cela ouvre la voie à une étude expérimentale des réponses neurales dans le cortex visuel primaire du chat à des stimuli multivariés. Nous découvrons de nouveaux types de neurones fonctionnels, dépendants de la couche corticale, qui peuvent être liés à la connectivité récurrente. (3) Nous démontrons que ce traitement de la variance peut être compris comme un graphe dynamique pondéré conditionné par la variance sensorielle, en utilisant des enregistrements du cortex visuel primaire du macaque. (4) Enfin, nous soutenons l'existence de calculs de variance (prédictifs) en dehors du cortex visuel primaire, par l'intermédiaire du noyau pulvinar du thalamus. Cela ouvre la voie à des études sur les calculs de variance en tant que calculs neuronaux génériques soutenus par la récurrence dans l'ensemble du cortex. / This thesis aims to understand the foundations and functions of the probabilistic computations involved in visual processes. We leverage a two-fold strategy, which involves the development of models within the framework of predictive coding under the free energy principle. These models serve to define clear hypotheses of neuronal function, which are tested using extracellular recordings of the primary visual cortex. This brain region is predominantly involved in computations on the elementary units of natural visual inputs, in the form of distributions of oriented edges. These probabilistic distributions, by nature, rely on processing both the mean and variance of a visual input. While the former have undergone extensive neurobiological scrutiny, the latter have been largely overlooked. This thesis aims to bridge this knowledge gap. We put forward the notion that intracortical recurrent connectivity is optimally suited for processing such variance of inputs, and our contributions to this idea are multi-faceted. (1) We first provide a computational examination of the orientation structure of natural images and associated neural encoding strategies. An empirical sparse model shows that the optimal neural code for representing natural images relies on orientation variance for increased efficiency, performance, and resilience. (2) This paves the way for an experimental investigation of neural responses in the cat's primary visual cortex to multivariate stimuli. We uncover novel, cortical-layer-dependent, functional neuronal types that can be linked to recurrent connectivity. (3) We demonstrate that this variance processing can be understood as a dynamical weighted graph conditioned on sensory variance, using macaque primary visual cortex recordings. (4) Finally, we argue for the existence of (predictive) variance computations outside the primary visual cortex, through the Pulvinar nucleus of the thalamus. This paves the way for studies on variance computations as generic weighting of neural computations, supported by recurrence throughout the entire cortex.