Generalized memory associativity in a network model for the neuroses

Wedemann, Roseli S.; Donangelo, R.; Carvalho, Luı́s Alfredo Vidal de

doi:10.1063/1.3099608

Cited by 20 publications

(25 citation statements)

References 26 publications

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“…We will also extend our results to stochastic Hopfield networks and Boltzmann machines. One area for further work is to examine the impact of strong attractors in the Boltzmann machine developed in [29] to model neuroses. A more challenging task is to integrate these conceptual tools with the current work on modelling cognitive-emotional decision making using attractor neural networks as in [30].…”

Section: Discussionmentioning

confidence: 99%

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Edalat

Mancinelli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We study the notion of a strong attractor of a Hopfield neural model as a pattern that has been stored multiple times in the network, and examine its properties using basic mathematical techniques as well as a variety of simulations. It is proposed that strong attractors can be used to model attachment types in developmental psychology as well as behavioural patterns in psychology and psychotherapy. We study the stability and basins of attraction of strong attractors in the presence of other simple attractors and show that they are indeed more stable with a larger basin of attraction compared with simple attractors. We also show that the perturbation of a strong attractor by random noise results in a cluster of attractors near the original strong attractor measured by the Hamming distance. We investigate the stability and basins of attraction of such clusters as the noise increases and establish that the unfolding of the strong attractor, leading to its breakup, goes through three different stages. Finally the relation between strong attractors of different multiplicity and their influence on each other are studied and we show how the impact of a strong attractor can be replaced with that of a new strong attractor. This retraining of the network is proposed as a model of how attachment types and behavioural patterns can undergo change.

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Section: Discussionmentioning

confidence: 99%

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Edalat

Mancinelli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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“…behavior. In [52], a Hopfield network with two weakly connected layers has been used to model the concept of working through in psychoanalysis.…”

Section: A Basic Neural Model Of Self-attachmentmentioning

confidence: 99%

Introduction to self-attachment and its neural basis

Edalat

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We introduce the notion of self-attachment which, based on an interdisciplinary set of concepts, proposes a new psychotherapeutic technique. The underlying ideas include findings and paradigms in developmental psychology and neuroscience, neuroplasticity and long term term potentiation, fMRI studies on human bond making, ethology and psychology of religion and experiments in energy based artificial neural networks. The proposed self-attachment therapeutic technique is distinguished by its intervention to create an internal and passionate affectional bond within the individual between the "adult self", representing the logical and cognitive faculty, and the "inner child", representing the unregulated and undeveloped emotional circuits. The aim is to create more optimal circuits for emotional regulation. The proposed self-attachment protocols internally emulate within the individual the interactions of a good enough primary care-giver and child in order to moderate the child's arousal level, minimise its negative affects and maximize its positive affects. These interactions are assumed, in developmental neuroscience and in developmental psychology, to be the basis of secure attachment of children with their parents, which leads to an optimal regulation of neurotransmitters, hormones, and the emotional dynamics of the individual. We report on several case studies of this technique in recent years. Finally, we propose a simple mathematical model to capture the impact of self-attachment protocols using the notion of strong patterns in energy based neural networks and employ a recently developed mathematical model to examine the impact of self-attachment using emotional and cognitive neural pathways for decision making.

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“…The purpose of the present work is to define, measure and study statistical properties of avalanches which occur during memory retrieval in an artificial neural network, developed to model neurotic phenomena and the associated conscious/unconscious interactions in mental life [1]. It was famously reported by Freud [2,3] that patients with neurotic symptoms systematically repeated these symptoms in the guise of ideas and impulses.…”

Section: Introductionmentioning

confidence: 99%

“…An illustrative, schematic model of this neurotic mechanism and the working-through therapeutic method, was advanced in [1,6]. It was there proposed that the neuroses can be understood in terms of an associative memory process in neural networks, where the network, when presented with an input pattern, retrieves a stored pattern which is most similar to the one currently shown.…”

Section: Introductionmentioning

confidence: 99%

“…Following the memory organization advanced in [7] (see also [1]), it is assumed that neurons belong to two hierarchically structured modules, respectively corresponding to sensorial and symbolic memories. Mental images of stimuli received by sensory receptors, either from the environment or from the body itself, are represented by memory traces stored in the sensorial memory module.…”

Section: Introductionmentioning

confidence: 99%

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Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

Siddiqui

Wedemann

2018

Physica A: Statistical Mechanics and its Applications

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We explore statistical characteristics of avalanches associated with the dynamics of a complexnetwork model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

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Generalized memory associativity in a network model for the neuroses

Cited by 20 publications

References 26 publications

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Introduction to self-attachment and its neural basis

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

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