A Neural Network Assembly Memory Model Based on an Optimal Binary Signal Detection Theory

A ternary/binary data coding algorithm and conditions under which Hopfield networks implement optimal convolutional or Hamming decoding algorithms has been described. Using the coding/decoding approach (an optimal Binary Signal Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model (NNAMM) is built. The model provides optimal (the best) basic memory performance and demands the use of a new memory unit architecture with two-layer Hopfield network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. NNAMM explicitly describes the dependence on time of a memory trace retrieval, gives a possibility of metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. A model of smallest inseparable part or an "atom" of consciousness is also defined. The NNAMM's neurobiological backgrounds and its applications to solving some interdisciplinary problems are shortly discussed. BSDT could implement the "best neural code" used in nervous tissues of animals and humans.

ROC Curves Within the Framework of Neural Network Assembly Memory Model: Some Analytic Results

On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.

A Neural Network Assembly Memory Model with Maximum-Likelihood Recall and Recognition Properties

It has been shown that a neural network model recently proposed to describe basic memory performance is based on a ternary/binary coding/decoding algorithm which leads to a new neural network assembly memory model (NNAMM) providing maximum-likelihood recall/recognition properties and implying a new memory unit architecture with Hopfield two-layer network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. For the data coding used, conditions are found under which a version of Hopfied network implements maximum-likelihood convolutional decoding algorithm and, simultaneously, linear statistical classifier of arbitrary binary vectors with respect to Hamming distance between vector analyzed and reference vector given. In addition to basic memory performance and etc, the model explicitly describes the dependence on time of memory trace retrieval, gives a possibility of one-trial learning, metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. It has been shown that an assembly memory unit may be viewed as a model of a smallest inseparable part or an 'atom' of consciousness. Some nontraditional neurobiological backgrounds (dynamic spatiotemporal synchrony, properties of time dependent and error detector neurons, early precise spike firing, etc) and the model's application to solve some interdisciplinary problems from different scientific fields are discussed.

Computer modeling of feelings and emotions: a quantitative neural network model of the feeling-of-knowing

The first quantitative neural network model of feelings and emotions is proposed on the base of available data on their neuroscience and evolutionary biology nature, and on a neural network human memory model which admits distinct description of conscious and unconscious mental processes in a time dependent manner. As an example, proposed model is applied to quantitative description of the feeling of knowing.

The tip-of-the-tongue phenomenon: Irrelevant neural network localization or disruption of its interneuron links ?

On the base of recently proposed three-stage quantitative neural network model of the tip-of-the-tongue (TOT) phenomenon a possibility to occur of TOT states coursed by neural network interneuron links' disruption has been studied. Using a numerical example it was found that TOTs coursed by interneron links' disruption are in (1.5 + - 0.3)x1000 times less probable then those coursed by irrelevant (incomplete) neural network localization. It was shown that delayed TOT states' etiology cannot be related to neural network interneuron links' disruption.

Three-Stage Quantitative Neural Network Model of the Tip-of-the-Tongue Phenomenon

A new three-stage computer artificial neural network model of the tip-of-the-tongue phenomenon is shortly described, and its stochastic nature was demonstrated. A way to calculate strength and appearance probability of tip-of-the-tongue states, neural network mechanism of feeling-of-knowing phenomenon are proposed. The model synthesizes memory, psycholinguistic, and metamemory approaches, bridges speech errors and naming chronometry research traditions. A model analysis of a tip-of-the-tongue case from Anton Chekhov's short story 'A Horsey Name' is performed. A new 'throw-up-one's-arms effect' is defined.

Quantitative Neural Network Model of the Tip-of-the-Tongue Phenomenon Based on Synthesized Memory-Psycholinguistic-Metacognitive Approach

A new three-stage computer artificial neural network model of the tip-of-the-tongue phenomenon is proposed. Each word's node is build from some interconnected learned auto-associative two-layer neural networks each of which represents separate word's semantic, lexical, or phonological components. The model synthesizes memory, psycholinguistic, and metamemory approaches, bridges speech errors and naming chronometry research traditions, and can explain quantitatively many tip-of-the-tongue effects.