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The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations.
In this paper, we provide an overview of abstract and biophysical models of associative memory with particular emphasis on the operations performed by the diverse (inter)neurons in encoding and retrieval of memories in the hippocampus.
Theoretical models of associative memory generally assume most of their parameters to be homogeneous across the network.
In the Discussion, we argue why the RW can be considered an appropriate a priori learning model for our particular paradigm, relative to other models of associative learning.
First, the goal of this fMRI study was not to pinpoint the exact mathematical form of incidental learning by comparing different models of associative learning.
The goal of this study was not to pinpoint the exact mathematical form of learning by comparing different models of associative learning.
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This resulted in a detailed spatio-temporal model of associative recognition.
I first describe a neural network model of associative memory in a small region of the brain.
The Hopfield neural network is a model of associative content-addressable memory with a simple flexible structure.
Over the last four decades, a model of associative recognition has been developed in the ACT-R cognitive architecture.
A model of associative friction sliding is proposed, expressed through a Differential Variational Inequality (DVI) formulation, relying upon the theory of Measure Differential Inclusion (MDI).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com