Simulation of Abnormal/Normal Brain States Using the KIV Model

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons.Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a model has been formulated that captures the initial epileptogenisis to the semi-periodic entrainment of distant neuron populations.The normal non-linear dynamic signal captured through EEG, moves into a semi-periodic state, which can be quantified as the seizure state.Capturing the asynchronous/synchronous behavior of the normal/pathological brain state will be discussed.This model will also demonstrate how electrical stimulation applied to the limbic system restores the seizure state of the brain back to its original normal condition.Human brain states are modeled using a biologically inspired neural network, the KIV model.The KIV model exhibits the noisy, chaotic attributes found in the limbic system of brains of higher forms of organisms, and in its normal basal state, represents the homogeneous activity of millions of neuron activations.The KIV can exhibit the ’unbalanced state’ of neural activity, whereas when a small cluster of abnormal firing neurons starts to exhibit periodic neural firings that eventually entrain all the neurons within the limbic system, the network has moved into the ‘seizure’ state.These attributes have been found in human EEG recordings and have been duplicated in this model of the brain.The discussion in this dissertation covers the attributes found in human EEG data and models these attributes.Additionally, this model proposes a methodology to restore the modeled ‘seizure’ state, and by doing so, proposes a manner for external electrical titration to restore the abnormal seizure state back to a normal chaotic EEG signal state.Quantification measurements of normal, abnormal, and restoration to normal brain states will be exhibited using the following approaches:Analysis of human EEG dataQuantification measurements of brain states.Development of models of the different brain states, i.e. fit parameters of the model on individual personal data/history.Implementation of quantitative measurements on “restored” simulated seizure state

A new type of self-organization associated with chaotic dynamics in neural networks

A new type of self-organized dynamics is presented, in relation with chaos in neural networks. One is chaotic itinerancy and the other is chaos-driven contraction dynam­ics. The former is addressed as a universal behavior in high-dimensional dynamical systems. In particuiar, it can be viewed as one possible form of memory dynamics in brain. The latter gives rise to singular-continuous nowhere-differentiable attractors. These dynamics can be related to each other in the context of dimentionality and of chaotic information processings. Possible roles of these oomplex dynamics in brain are also discussed

Un réseau de neurones à décharges pour la reconnaissance de processus spatio-temporels

Traitement des processus dynamiques non stationnaires dans les réseaux de neurones -- Traitement de l'information dans les systèmes nerveux biologiques -- Modèle du réseau de neurones à décharges -- Modèle du neuronne -- Architecture et apprentissage -- Activité d'auto-organisation -- Application à la reconnaisance des chiffres bruités -- Réseau avec mécanisme de > avec récompense -- Traitement des séquences temporelles et détection de mouvement -- Traitement des séquences temporelles -- Détection de mouvement -- Prototype pour un système d'identification du locuteur à l'aide du réseau proposé -- Analyse de la parole par modulation d'amplitude dans le système auditif -- Système d'identification du locuteur -- Traitement des enveloppes par le réseau proposé -- Identification du locuteur basée sur les paramètres de sortie du réseau proposé

Coding and learning of chemosensor array patterns in a neurodynamic model of the olfactory system

Arrays of broadly-selective chemical sensors, also known as electronic noses, have been developed during the past two decades as a low-cost and high-throughput alternative to analytical instruments for the measurement of odorant chemicals. Signal processing in these gas-sensor arrays has been traditionally performed by means of statistical and neural pattern recognition techniques. The objective of this dissertation is to develop new computational models to process gas sensor array signals inspired by coding and learning mechanisms of the biological olfactory system. We have used a neurodynamic model of the olfactory system, the KIII, to develop and demonstrate four odor processing computational functions: robust recovery of overlapping patterns, contrast enhancement, background suppression, and novelty detection. First, a coding mechanism based on the synchrony of neural oscillations is used to extract information from the associative memory of the KIII model. This temporal code allows the KIII to recall overlapping patterns in a robust manner. Second, a new learning rule that combines Hebbian and anti-Hebbian terms is proposed. This learning rule is shown to achieve contrast enhancement on gas-sensor array patterns. Third, a new local learning mechanism based on habituation is proposed to perform odor background suppression. Combining the Hebbian/anti-Hebbian rule and the local habituation mechanism, the KIII is able to suppress the response to continuously presented odors, facilitating the detection of the new ones. Finally, a new learning mechanism based on anti-Hebbian learning is proposed to perform novelty detection. This learning mechanism allows the KIII to detect the introduction of new odors even in the presence of strong backgrounds. The four computational models are characterized with synthetic data and validated on gas sensor array patterns obtained from an e-nose prototype developed for this purpose