Computational Models of Dementia and Neurological Problems

Abstract

The final goal of neuroscience is to fully understand neural processes, their relations to mental processes and to cognitive, affective, and behavioral disorders. Computational modeling, although still in its infancy, already plays a central role in this endeavor. A review of different aspects of computational models that help to explain many features of neuropsychological syndromes and psychiatric disease is presented. Recent advances in computational modeling of epilepsy, cortical reorganization after lesions, Parkinson's and Alzheimer diseases are reviewed. Some trends in computational models of brain functions are identified.Key words: neural networks; cognitive computational neuroscience; associative memory models, cortical reorganization, computational models in psychiatry, Parkinson disease, Alzheimer disease. A bit of historyNeuroinformatics has two large branches. On the one hand it provides tools for storage and analysis of information generated by neuroscience. On the other hand it provides simulations and models that capture some aspects of information processing in the brain. Complexity of the brain dynamics may be too high to understand brain's functions in details in conceptual terms. Computational models based on correct principles may capture progressively larger number of essential features of brain dynamics, eventually leading to models of the whole brain that no individual expert will ever be able to understand in details. This situation is analogous to the cell biology, where the sheer number of biomolecules and their interactions will prevent experts from understanding all genetic and metabolic mechanism of a living cell. From the engineering perspective understanding a complex system implies the ability to build a model that behaves in important aspects in the same way as the system that is being modeled. At this point in history computer simulations are the easiest way to build complex models, but progress in building neuromorphic devices that implement some neural functions in hardware may change this situation in future (1).In 1986 two volumes "Parallel Distributed Processing: Explorations in the Microstructure of Cognition" (2), written mostly by psychologist, were published. The first volume of the PDP book (as it was commonly called) focused on general properties of parallel information processing, drawing analogies with human information processing. The Parallel Distributed Processing (PDP) name did not gain popularity, replaced by "connectionism", the name that stressed the importance of connections that pass information between network of nodes representing neural cell assemblies, increasing or inhibiting their activations. One of the chapters described now famous "backpropagation of errors" algorithm that can be used to train a network of simple artificial neurons with a one-way (feedforward) signal flow desired responses to incoming signals. Such artificial neural networks became very useful for medical diagnostics support, signal and image analysis and monitoring, searc...

A neural model of hippocampal–striatal interactions in associative learning and transfer generalization in various neurological and psychiatric patients

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Modeling Alzheimer’s disease: from past to future

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Perspectives for computational modeling of cell replacement for neurological disorders

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