Sentence examples for memory networks with from inspiring English sources

In this Letter, we study BAM (bidirectional associative memory) networks with variable coefficients.

We evaluate this learning rule for sequence memory networks with instantaneous feedback inhibition and show that little surprisingly, memory capacity degrades with increased variability in sparseness.

To support design frequency as a measure of spreading activation, we conducted the second study which involved correlating word frequency, our measure of spreading activation in lexical and semantic memory networks, with design frequency.

Memory networks with variable sparseness have been studied by Amit and Huang [15, 16] under a different learning paradigm in which old memories are gradually overwritten by new memories, and for several more involved synaptic (meta- plasticity rules.

Motivated by the above discussions, the objective of this paper is to study the global exponential stability of the following Cohen-Grossberg bidirectional associative memory networks with impulses and time delays on time scales: (1.1).

This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks with or without delays.

Better performance in the young adults was associated with the capability to modulate the regions of the working memory network with increasing task difficulty, however enhanced performance in the older cohort was associated with greater load-induced deactivation of the posterior cingulate cortex.

We used a visuospatial working memory functional MRI paradigm to investigate: (i) postoperative recovery and reorganization of working memory networks in patients with left and right TLE before and 3 and 12 months after ATLR compared with healthy control subjects; and (ii) the efficiency of these postoperative changes in working memory networks.

The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

Specifically, the DSMM applys bidirectional Long Short Term Memory Network (BiLSTM) with multi-granularities to match mentions with candidate entities from two aspects: surface form match by a character-level BiLSTM (char-LSTM) and semantic match based on the "structural" context of entities and the textual context of mentions by a word-level BiLSTM (word-LSTM).

On the other hand, a hyperactivation of memory networks was found in subjects with MCI with clinical memory impairment (Dickerson et al., 2005; Bokde et al., 2008, 2009).