6,646 research outputs found
A model of the emergence and evolution of integrated worldviews
It \ud
is proposed that the ability of humans to flourish in diverse \ud
environments and evolve complex cultures reflects the following two \ud
underlying cognitive transitions. The transition from the \ud
coarse-grained associative memory of Homo habilis to the \ud
fine-grained memory of Homo erectus enabled limited \ud
representational redescription of perceptually similar episodes, \ud
abstraction, and analytic thought, the last of which is modeled as \ud
the formation of states and of lattices of properties and contexts \ud
for concepts. The transition to the modern mind of Homo \ud
sapiens is proposed to have resulted from onset of the capacity to \ud
spontaneously and temporarily shift to an associative mode of thought \ud
conducive to interaction amongst seemingly disparate concepts, \ud
modeled as the forging of conjunctions resulting in states of \ud
entanglement. The fruits of associative thought became ingredients \ud
for analytic thought, and vice versa. The ratio of \ud
associative pathways to concepts surpassed a percolation threshold \ud
resulting in the emergence of a self-modifying, integrated internal \ud
model of the world, or worldview
The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing
We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of
morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is
a powerful tool for integrating behavioural and neurophysiological results
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
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.Comment: Proceedings of the KDS-2003 Conference held in Varna, Bulgaria on
June 16-26, 2003, pages 138-146, 5 Figures, 18 reference
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
The inapplicability of amino acid covariation methods to small protein
families has limited their use for structural annotation of whole genomes.
Recently, deep learning has shown promise in allowing accurate residue-residue
contact prediction even for shallow sequence alignments. Here we introduce
DMPfold, which uses deep learning to predict inter-atomic distance bounds, the
main chain hydrogen bond network, and torsion angles, which it uses to build
models in an iterative fashion. DMPfold produces more accurate models than two
popular methods for a test set of CASP12 domains, and works just as well for
transmembrane proteins. Applied to all Pfam domains without known structures,
confident models for 25% of these so-called dark families were produced in
under a week on a small 200 core cluster. DMPfold provides models for 16% of
human proteome UniProt entries without structures, generates accurate models
with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor
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