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This study evaluates individual differences in hypnotizability as reflected in waking-state hemispheric engagement during recollection of 3 positively and 3 negatively valenced personal life events. The State-Trait Anxiety Inventory, Maudsley Personality Inventory, Tellegen Absorption Scale and Harvard Group Scale of Hypnotic Susceptibility (Form A) were… (More)
Quantum noise, entanglement and chaos in the quantum field theory of mind/brain states Abstract We review the dissipative quantum model of brain and present recent developments related with the rôle of entanglement, quantum noise and chaos in the model.
Quantum noise induced entanglement and chaos in the dissipative quantum model of brain Abstract We discuss some features of the dissipative quantum model of brain in the frame of the formalism of quantum dissipation. Such a formalism is based on the doubling of the system degrees of freedom. We show that the doubled modes account for the quantum noise in… (More)
Inspired by the dissipative quantum model of brain, we model the states of neural nets in terms of collective modes by the help of the formalism of Quantum Field Theory. We exhibit an explicit neural net model which allows to memorize a sequence of several informations without reciprocal destructive interference, namely we solve the overprinting problem in… (More)
We show that particular features of prosopagnosic impairment can be simulated by a connectionist model trained with an unsupervised learning procedure. In particular we describe a Kohonen's neural network which is able to correctly recognize and categorize a series of digitized pictures of faces when learning is characterized by certain parameter values,… (More)
The aim of this editorial is to briefly introduce some papers of different nature presented by the contributors to the special issue on " Second Generation General System Theory ". These contributions have been focused on the need for building a post-Bertalanffy Systemics, based on new problems, representations, and approaches to complexity. Furthermore,… (More)
The interest for neural networks stems from the fact that they appear as universal approximators for whatever kind of nonlinear dynamical system of arbitrary complexity. Because nonlinear systems, are studied mainly to model self-organization and emergence phenomena, there is the hope that neural networks can be used to investigate these phenomena in a… (More)