Aperiodic dynamics are known to be essential in the formation of perceptual mechanisms and representations in biological organisms. Advances in neuroscience and computational neurodynamics are helping us understand the properties of nonlinear systems that are fundamental in the self-organization of stable, complex patterns in many types of systems, from biological ecosystems to human economies and in biological brains. In this paper we introduce a neurological population model that is capable of replicating the important aperiodic dynamics observed in biological brains. We use the mechanism to self-organize cognitive maps in an autonomous agent.

Biological brains are saturated with complex dynamics. Artificial neural network models abstract much of this complexity away and represent the computational process of neuronal groups in terms of simple point, and sometimes periodic attractors.

This dissertation attempts to shed new light on the mechanisms used by human subjects to extract features from visual stimuli and for their subsequent classification. A methodology combining human psychophysics and machine learning is introduced, where feature extractors are modeled using methods from unsupervised machine learning whereas supervised machine learning is considered for classification. We consider a gender classification task using stimuli drawn from the Max Planck Institute face database. Once a feature extractor is chosen and the corresponding data representation is computed, the resulting feature vector is classified using a separating hyperplane (SH) between the classes. The behavioral responses of humans to one stimulus, in our study the gender estimate and its corresponding reaction time and confidence rating, are compared and correlated to the distance of the feature vector of this stimulus to the SH. It is successfully demonstrated that machine learning can be used as a novel method to “look into the human

Biological brains are saturated with complex dynamics. Artificial neural network models abstract much of this complexity away and represent the computational process of neuronal groups in terms of simple point, and sometimes periodic attractors. But is this abstraction justified? Aperiodic dynamics are known to be essential in the formation of perceptual mechanisms and representations in biological organisms. Advances in neuroscience and computational neurodynamics are helping us to understand the properties of nonlinear systems that are fundamental in the self-organization of stable, complex patterns for perceptual, memory and other cognitive mechanisms in biological brains. Much of this new understanding of the principles of selforganization in biological brains has yet to be modeled or used to improve the performance of autonomous robotic and virtual agents. In this paper we present a model of an autonomous agent learning appetitive/aversive behaviors using a neuronal group model capable of such aperiodic dynamics. We demonstrate how such dynamics are useful in the self-organization of perception and behavior, and discuss the use of aperiodic dynamics in the self-organization of cognitive mechanisms in autonomous agents. I.

Abstract I propose in this chapter that the formation of global neurocognitive state in the cerebral cortex is central to the mammalian capacity for assessment of organismic state. I consider a putative mechanism for the formation of global neurocognitive state from interactions among interconnected cortical areas. In this model, each area makes a local assessment of its own current state, representing a partial assessment of organismic state, through the generation of packets of high-frequency oscillatory wave activity. The spatial amplitude modulation (AM) pattern of the wave packet is proposed to represent the expression of an area’s current state in relation to the other areas with which it is interacting. Through their interactions, sets of cortical areas mutually constrain the AM patterns of their wave packets. It is proposed that this process leads to the manifestation of wave packets having cognitively consistent patterns, and the formation of globally unified consensual neurocognitive states. 1

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Psychoanalysis and the brain – why did Freud abandon