Neural modelling of psychiatric disorders

Ruppin, Eytan

doi:10.1088/0954-898x/6/4/007

Cited by 11 publications

(7 citation statements)

References 82 publications

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“…The distance between attractors and cognition is to be filled by bold speculations. Some imaginative speculations have been put forward (see Hoffman, and the review by Ruppin 1995). In my view, these are still too naive and put too much emphasis on generating cognitive interpretations based on single networks and on properties that they possess only at a very specific stage of modeling.…”

Section: R10 Attractors and Cognitionmentioning

confidence: 99%

The Hebbian paradigm reintegrated: Local reverberations as internal representations

1995

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The neurophysiological evidence from the Miyashita group's experiments on monkeys as well as cognitive experience common to us all suggests that local neuronal spike rate distributions might persist in the absence of their eliciting stimulus. In Hebb's cell-assembly theory, learning dynamics stabilize such self-maintaining reverberations. Quasi-quantitive modeling of the experimental data on internal representations in association-cortex modules identifies the reverberations (delay spike activity) as the internal code (representation). This leads to cognitive and neurophysiological predictions, many following directly from the language used to describe the activity in the experimental delay period, others from the details of how the model captures the properties of the internal representations.

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Section: R10 Attractors and Cognitionmentioning

confidence: 99%

The Hebbian paradigm reintegrated: Local reverberations as internal representations

1995

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“…where the neighborhood is usually assumed to be Gaussian: (4) The size of this neighborhood is decreased from the initial value of dispersion  i to the final value  f according to the formula: (5) The maximal age t max determines the annealing schedule. The learning rate is similarly decreased by: (6) SOM model has been used with success in many applications, for example it works quite well, in comparison to other neural models, for explanation of details of orientation and ocular dominance columns in the visual cortex [25].…”

Section:  mentioning

confidence: 99%

“…The central role of large-scale neural dynamics as a basis for understanding brain processes is now well recognized. 26 , 27 The two main goals of this paper are thus to show that memetics may be based on solid theoretical foundations grounded in neurodynamical models, and that learning using simple memory models may help to understand the process of formation of conspiracy theories. Although only simple competitive learning models are used in this paper, it should open the road toward application of more complex neural models that link memetics with neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Memetics and neural models of conspiracy theories

Duch

2021

Patterns

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Memetics has so far been developing in social sciences, but to fully understand memetic processes it should be linked to neuroscience models of learning, encoding, and retrieval of memories in the brain. Attractor neural networks show how incoming information is encoded in memory patterns, how it may become distorted, and how chunks of information may form patterns that are activated by many cues, forming the foundation of conspiracy theories. The rapid freezing of high neuroplasticity (RFHN) model is offered as one plausible mechanism of such processes. Illustrations of distorted memory formation based on simulations of competitive learning neural networks are presented as an example. Linking memes to attractors of neurodynamics should help to give memetics solid foundations, show why some information is easily encoded and propagated, and draw attention to the need to analyze neural mechanisms of learning and memory that lead to conspiracies.

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“…They provide insights into possible causal relations, allow for a full control of all aspects of experiments, they are inexpensive and are not restricted by ethical considerations. An early review article (28) and the books (5,6,10,22,26,29) provide many insights into the mechanisms behind the memory and language impairments, psychiatric disorders, Alzheimer and Parkinson disease, epilepsy and other neurological problems.…”

Section: Brain Simulations and Mental Functionsmentioning

confidence: 99%

Computational Models of Dementia and Neurological Problems

Duch

2007

Methods in Molecular Biology™

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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...

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Neural modelling of psychiatric disorders

Cited by 11 publications

References 82 publications

The Hebbian paradigm reintegrated: Local reverberations as internal representations

The Hebbian paradigm reintegrated: Local reverberations as internal representations

Memetics and neural models of conspiracy theories

Computational Models of Dementia and Neurological Problems

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