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A unified associative memory model with a novel method for designing associative memories is presented in this paper.

Reference [110] extends the previously studied CRF-LSTM (conditional random field, long short-term memory) model with explicit modeling of pairwise potentials and also proposes an approximate version of skip-chain CRF inference with RNN potentials.

The large scale organization of the architecture is based on a multi-component working memory model, with a central executive that controls the flow of information among the slave systems through neural gating mechanisms.

A memory model with Store Atomicity is serializable: there is a unique global interleaving of all operations which respects the reordering rules and serializes all the operations in a transaction together.

We tested the activation of p-CREB in the ACC of the pain memory model with IF and EMSA.

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We link these matrix associative memory models with the mathematics that underlie functional neuroimaging techniques and present the "functional brain images" emerging from the model.

We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis.

Our framework assumes a shared-memory model with similar properties (multi-threaded, shared address space, etc).

This chapter illustrates distributed shared memory (DSM) systems that are intended to combine the ease of programming of the shared-memory model with the scalability of the distributed-memory system.

By accessing the data from external sources, the data retrieved are loaded in the memory of the system and the data are transformed into in-memory models with the use of a "data to rdf" (Listing 2) mechanism that was implemented internally.

This concept has much in common with the "central executive" component of the working memory model which interacts with short-term verbal and visuospatial stores to manipulate information during complex decision making (Baddeley and Hitch 1974; Baddeley 2001).

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