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Encoding (memory)

About: Encoding (memory) is a research topic. Over the lifetime, 7547 publications have been published within this topic receiving 120214 citations. The topic is also known as: memory encoding & encoding of memories.


Papers
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Journal ArticleDOI
TL;DR: The FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall and an extra operator namely negative operator is added to achieve this objective.
Abstract: Human brain represents the information and stores it as memory. They are stored in different parts of the brain and are linked together by associations. When a cue is provided, the memory is recalled through association. Encoding of the real world information is in the form of object-attribute relation. It is possible to perform both positive recall (object having the attribute and attribute shared by object) and negative recalls (object not having the attribute and attribute not shared by object) from memory. It is evident from literature that the formal concept analysis (FCA) based on bidirectional associative memory (BAM) performs only positive recall from memory. In this paper, FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall. In this extended model, both positive recall and negative recall are performed. In order to achieve this objective, an extra operator namely negative operator is added. The proposed model is validated with an experiment on real world scenario. We also presented the connection of the proposal with long term potentiation (LTP) and Hippocampus of the human brain.

57 citations

Journal ArticleDOI
TL;DR: This article examined the relationship between response accuracy and response latency as measures of memory, and questions are raised concerning the value of the unidimensionality assumption often invoked in theories of memory.

57 citations

Patent
Peter K. Naji1
08 Dec 1999
TL;DR: In this paper, the state of each cell in a stacked memory comprising stacks of cells in an addressable array with each stack including MTJ memory cells stacked together with current terminals connected in series, and including a first and second current terminals coupled through an electronic switch to a current source.
Abstract: Apparatus and method of reading the state of each cell in a stacked memory comprising stacks of cells in an addressable array with each stack including MTJ memory cells stacked together with current terminals connected in series, and including a first and second current terminals coupled through an electronic switch to a current source. Each stack includes 2 n levels of memory. A voltage drop across an addressed stack is sensed. Reference voltages equal to the 2 n memory levels are provided and the sensed voltage drop is compared to the reference voltages to determine the memory level in the addressed stack. Encoding apparatus is used to convert the voltage drop to a digital output signal.

57 citations

Patent
16 Jun 1993
TL;DR: In this paper, the authors proposed a method to derive a pair of analysis/synthesis windows from a known window function which satisfy various filter selectivity and window overlap-add constraints.
Abstract: The invention relates to the design of analysis and synthesis windows for use in high-quality transform encoding and decoding of audio signals, especially encoding and decoding having a short signal-propagation delay. The design method derives a pair of analysis/synthesis windows from a known window function which satisfy various filter selectivity and window overlap-add constraints.

57 citations

Journal ArticleDOI
01 Dec 1989
TL;DR: A method of vector quantisation which trades off accuracy for speed of encoding is presented, which finds that there is little loss in encoding accuracy, when compared with the exact nearest neighbour encoding using an equivalent single stage encoder.
Abstract: We present a method of vector quantisation which trades off accuracy for speed of encoding. We achieve this by hierarchically structuring a multistage encoder so that each stage encodes low dimensional input vectors. Such hierarchical encoders may easily be realised as a set of fast table look-up operations. We demonstrate how the Euclidean distortion in such a multistage encoder is approximately minimised by using Kohonen's topographic mapping learning algorithm from neural network theory. We also demonstrate the performance of the technique on various stochastic time series. We find that there is little loss in encoding accuracy, when compared with the exact nearest neighbour encoding using an equivalent single stage encoder.

57 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,083
20222,253
2021450
2020378
2019358
2018363