Platonic model of mind as an approximation to neurodynamics

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view

Computational physics of the mind

In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures

Neural implementation of psychological spaces

Psychological spaces give natural framework for construction of mental representations. Neural model of psychological spaces provides a link between neuroscience and psychology. Categorization performed in high-dimensional spaces by dynamical associative memory models is approximated with low-dimensional feedforward neural models calculating probability density functions in psychological spaces. Applications to the human categorization experiments are discussed

Simple cyclic movements as a distinct autism feature - computational approach

Diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders(ASD). Each year percentage of children diagnosed with ASD is growing. One common diag-nostic feature in individuals with ASD is the tendency to atypical simple cyclic movements.The motor brain activity seems to generate periodic attractor state that is hard to escape.Despite numerous studies scientists and clinicians do not know exactly if ASD is a result ofa simple but general mechanism, or a complex set of mechanisms, both on neural, molecularand system levels. Simulations using biologically relevant neural network model presentedhere may help to reveal simplest mechanisms that may be responsible for specific behavior.Abnormal neural fatigue mechanisms may be responsible for motor as well as many if notall other symptoms observed in ASD

Autism and ADHD – two ends of the same spectrum?

Analysis of dynamics of biologically motivated neural networks allows for studying non-linear processes responsible for cognitive functions and thus provides adequate language to understand complex mental processes, including psychiatric syndromes and disorders. Problems with attention shifts that are at the roots of Autism Spectrum Disorders (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD), have been investigated using network model of Posner Visual Orienting Task (PVOT). Changing parameters that control biophysical properties of model neurons and cause network dysfunctions provides plausible explanations of many strange ASD and ADHD phenomena

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

COMPUTATIONAL APPROACH TO UNDERSTANDING AUTISM SPECTRUM DISORDERS

Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level? The hypothesis advanced in this paper is focused on neural dysfunctions that lead to problems with attention in autistic people. Simulations of attractor neural networks performing cognitive functions help to assess system long-term neurodynamics. The Fuzzy Symbolic Dynamics (FSD) technique is used for the visualization of attractors in the semantic layer of the neural model of reading. Large-scale simulations of brain structures characterized by a high order of complexity requires enormous computational power, especially if biologically motivated neuron models are used to investigate the influence of cellular structure dysfunctions on the network dynamics. Such simulations have to be implemented on computer clusters in a grid-based architecture