The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Matti Keinänen - One of the best experts on this subject based on the ideXlab platform.
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On Symbolic Function and its role in psychoanalytic psychotherapy
Psychoanalytic Psychotherapy, 2001Co-Authors: Matti KeinänenAbstract:SUMMARY One of the curative factors of psychotherapy is that the patient internalises, on the one hand, the therapist's attitude towards himself; and, on the other hand, he/she creates channels and the means to elaborate unmet needs in his/her internal experiential world. I consider the internalisation process in this study in the framework of a gradual symbolisation-reflectiveness process, using the tripartite model of Charles Peirce to conceptualise symbolisation and apply it to an understanding of the evolving psychic process. My aim is to analyse the organisation of symbolisation-reflectiveness in the psychoanalytic psychotherapy of my patient Thomas, illustrating this with material from the sessions. Thomas's central conflict was connected with early unprocessed separation experiences in the mother-child relationship. These separation experiences manifested themselves as inexplicable panic attacks during the therapy. In the potential space created by basic trust, Thomas was able to integrate feelings...
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Internalization and symbolization in the process of psychoanalytic psychotherapy: A case study
Nordic Journal of Psychiatry, 2000Co-Authors: Matti KeinänenAbstract:My aim is to describe the therapeutic effect of psychoanalytic psychotherapy by estimating the psychotherapeutic relationship from the viewpoint of symbolization? reflectiveness, which forms the framework for my consideration. The psychic development takes place as an evolution of Symbolic and reflective Functions, which proceed as an epigenetic process in successful psychotherapy. I formulate Symbolic Function by using Charles Peirce's sign terminology. These signs are index, icon, and conventional symbol, which form the corresponding three modes of Symbolic Function. The human mind binds physical observations, which come from him:herself and:or from the outer world, by means of Symbolic Function and the reflective capacity; thus psychic structures and meanings are formed. A model of symbolization?reflectiveness in the psyche is presented. A longitudinal psychotherapy process is described to illustrate the patient's capacity to process the undifferentiated anxiety into psychic differentiation by means of...
M Keinanen - One of the best experts on this subject based on the ideXlab platform.
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The meaning of the Symbolic Function in psychoanalytic psychotherapy: clinical theory and psychotherapeutic applications.
The British journal of medical psychology, 1997Co-Authors: M KeinanenAbstract:This study presents the clinical theory and psychotherapeutic applications of Symbolic Function in psychoanalytic psychotherapy. The three modes of Symbolic Function (indexical, iconic and conventional Symbolic) form the representations of the self and the object and further the unconscious fantasy, which includes the affective bond between these representations. During his/her development the child absorbs from the mother a new ego capacity (by means of identification), which I call the reflective-integration capacity. Then, the ego of the child has two crucial Functions: the fantasy world adopted through the Symbolic Function and the reflective-integrative capacity. In psychoanalytic psychotherapy our aim is to study the fantasy world of the patient by means of the reflective-integrative capacity, or if this capacity is missing or weak, to promote its development. During psychoanalytic psychotherapy a shared area of reflection-integration is formed, in which the healing changes occur. These healing changes include mainly the formation, consolidation and enrichment of the Symbolic Function of the patient. The consolidation of Symbolic Function and the capacity to move within the different modes of Symbolic Function occur in the area of psychology in which there are incapabilities and/or conflicts (e.g. separation anxiety) in the patient. Clinical case material is presented to illustrate these phenomena.
Amir F. Atiya - One of the best experts on this subject based on the ideXlab platform.
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Symbolic Function Network: Theory and Implementation
Artificial Higher Order Neural Networks for Modeling and Simulation, 2013Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:This chapter reviews a recent HONN-like model called Symbolic Function Network (SFN). This model is designed with the goal to impart more flexibility than both traditional and HONNs neural networks. The main idea behind this scheme is the fact that different Functional forms suit different applications and that no specific architecture is best for all. Accordingly, the model is designed as an evolving network that can discover the best Functional basis, adapt its parameters, and select its structure simultaneously. Despite the high modeling capability of SFN, it is considered as a starting point for developing more powerful models. This chapter aims to open a door for researchers to propose new formulations and techniques that impart more flexibility and result in sparser and more accurate models. Through this chapter, the theoretical basis of SFN is discussed. The model optimization computations are deeply illustrated to enable researchers to easily implement and test the model.
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Symbolic Function Network: Application to Telecommunication Networks Prediction
Artificial Higher Order Neural Networks for Modeling and Simulation, 2013Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:Quality of Service (QoS) of telecommunication networks could be enhanced by applying predictive control methods. Such controllers rely on utilizing good and fast (real-time) predictions of the network traffic and quality parameters. Accuracy and recall speed of the traditional Neural Network models are not satisfactory to support such critical real time applications. The Symbolic Function Network (SFN) is a HONN-like model that was originally motivated by the current needs of developing more enhanced and fast predictors for such applications. In this chapter, the authors use the SFN model to design fast and accurate predictors for the telecommunication networks quality control applications. Three predictors are designed and tested for the network traffic, packet loss, and round trip delay. This chapter aims to open a door for researchers to investigate the applicability of SFN in other prediction tasks and to develop more accurate and faster predictors.
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Symbolic Function network
Neural networks : the official journal of the International Neural Network Society, 2009Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:In this paper a model called Symbolic Function network (SFN) is introduced; that is based on using elementary Functions (for example powers, the exponential Function, and the logarithm) as building blocks. The proposed method uses these building blocks to synthesize a Function that best fits the training data in a regression framework. The resulting network is of the form of a tree, where adding nodes horizontally means having a summation of elementary Functions and adding nodes vertically means concatenating elementary Functions. Several new algorithms were proposed to construct the tree based on the concepts of forward greedy search and backward greedy search, together with applying the steepest descent concept. The method is tested on a number of examples and it is shown to exhibit good performance.
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Round Trip Time Prediction Using the Symbolic Function Network Approach
arXiv: Neural and Evolutionary Computing, 2008Co-Authors: George S. Eskander, Amir F. Atiya, Kil To Chong, Hyongsuk Kim, Sung Goo YooAbstract:In this paper, we develop a novel approach to model the Internet round trip time using a recently proposed Symbolic type neural network model called Symbolic Function network. The developed predictor is shown to have good generalization performance and simple representation compared to the multilayer perceptron based predictors.
George S. Eskander - One of the best experts on this subject based on the ideXlab platform.
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Symbolic Function Network: Theory and Implementation
Artificial Higher Order Neural Networks for Modeling and Simulation, 2013Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:This chapter reviews a recent HONN-like model called Symbolic Function Network (SFN). This model is designed with the goal to impart more flexibility than both traditional and HONNs neural networks. The main idea behind this scheme is the fact that different Functional forms suit different applications and that no specific architecture is best for all. Accordingly, the model is designed as an evolving network that can discover the best Functional basis, adapt its parameters, and select its structure simultaneously. Despite the high modeling capability of SFN, it is considered as a starting point for developing more powerful models. This chapter aims to open a door for researchers to propose new formulations and techniques that impart more flexibility and result in sparser and more accurate models. Through this chapter, the theoretical basis of SFN is discussed. The model optimization computations are deeply illustrated to enable researchers to easily implement and test the model.
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Symbolic Function Network: Application to Telecommunication Networks Prediction
Artificial Higher Order Neural Networks for Modeling and Simulation, 2013Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:Quality of Service (QoS) of telecommunication networks could be enhanced by applying predictive control methods. Such controllers rely on utilizing good and fast (real-time) predictions of the network traffic and quality parameters. Accuracy and recall speed of the traditional Neural Network models are not satisfactory to support such critical real time applications. The Symbolic Function Network (SFN) is a HONN-like model that was originally motivated by the current needs of developing more enhanced and fast predictors for such applications. In this chapter, the authors use the SFN model to design fast and accurate predictors for the telecommunication networks quality control applications. Three predictors are designed and tested for the network traffic, packet loss, and round trip delay. This chapter aims to open a door for researchers to investigate the applicability of SFN in other prediction tasks and to develop more accurate and faster predictors.
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Symbolic Function network
Neural networks : the official journal of the International Neural Network Society, 2009Co-Authors: George S. Eskander, Amir F. AtiyaAbstract:In this paper a model called Symbolic Function network (SFN) is introduced; that is based on using elementary Functions (for example powers, the exponential Function, and the logarithm) as building blocks. The proposed method uses these building blocks to synthesize a Function that best fits the training data in a regression framework. The resulting network is of the form of a tree, where adding nodes horizontally means having a summation of elementary Functions and adding nodes vertically means concatenating elementary Functions. Several new algorithms were proposed to construct the tree based on the concepts of forward greedy search and backward greedy search, together with applying the steepest descent concept. The method is tested on a number of examples and it is shown to exhibit good performance.
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Round Trip Time Prediction Using the Symbolic Function Network Approach
arXiv: Neural and Evolutionary Computing, 2008Co-Authors: George S. Eskander, Amir F. Atiya, Kil To Chong, Hyongsuk Kim, Sung Goo YooAbstract:In this paper, we develop a novel approach to model the Internet round trip time using a recently proposed Symbolic type neural network model called Symbolic Function network. The developed predictor is shown to have good generalization performance and simple representation compared to the multilayer perceptron based predictors.
Francesco D'errico - One of the best experts on this subject based on the ideXlab platform.
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What processes sparked off Symbolic representations? A reply to Hodgson and an alternative perspective
Journal of Archaeological Science: Reports, 2019Co-Authors: Emmanuel Mellet, Ivan Colagè, Andrea Bender, Christopher S. Henshilwood, K. Hugdahl, Torill Christine Lindstrøm, Francesco D'erricoAbstract:Abstract The Neurovisual Resonance Theory (NRT) proposes a framework for interpreting the earliest abstract engravings. It postulates that the first engraved marks produced by hominins reflected preferences of the early visual cortex for simple geometric patterns and served aesthetic rather than Symbolic purposes. In a recent article published in this journal the proponent of this theory provides a synthesis of neuroimaging studies that he understands as supporting his theory while criticising a recent neuroimaging study conducted by some of us, which explores the possible Symbolic Function of the earliest engraved marks. In this paper, we point to a broader range of literature backing up our interpretation, scrutinize theoretical claims put forward by Hodgson, and test them against published and yet unpublished empirical evidence. We conclude that these data support the hypothesis that the earliest engravings served a representational purpose and may have served a Symbolic Function.
