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.

Nonvolatile memory with modulated error correction coding

Abstract: Data is stored in a nonvolatile memory so that different pages of data stored in the same memory cells are encoded according to different encoding schemes. A first page is decoded according to its encoding scheme and an output is provided based on the decoding of the first page that is subsequently used in decoding a second page.

A Binary Learning Framework for Hyperdimensional Computing

Abstract: Brain-inspired Hyperdimensional (HD) computing is a computing paradigm emulating a neuron’s activity in high-dimensional space. In practice, HD first encodes all data points to high-dimensional vectors, called hypervectors, and then performs the classification task in an efficient way using a well-defined set of operations. In order to provide acceptable classification accuracy, the current HD computing algorithms need to map data points to hypervectors with non-binary elements. However, working with non-binary vectors significantly increases the HD computation cost and the amount of memory requirement for both training and inference. In this paper, we propose BinHD, a novel learning framework which enables HD computing to be trained and tested using binary hypervectors. BinHD encodes data points to binary hypervectors and provides a framework which enables HD to perform the training task with significantly low resources and memory footprint. In inference, BinHD binarizes the model and simplifies the costly Cosine similarity used in existing HD computing algorithms to a hardware-friendly Hamming distance metric. In addition, for the first time, BinHD introduces the concept of learning rate in HD computing which gives an extra knob to the HD in order to control the training efficiency and accuracy. We accordingly design a digital hardware to accelerate BinHD computation. Our evaluations on four practical classification applications show that BinHD in training (inference) can achieve 12.4× and 6.3× (13.8× and 9.9×) energy efficiency and speedup as compared to the state-of-the-art HD computing algorithm while providing the similar classification accuracy.

A retrieved context model of the emotional modulation of memory.

Abstract: Emotion enhances episodic memory, an effect thought to be an adaptation to prioritize the memories that best serve evolutionary fitness. However, viewing this effect largely in terms of prioritizing what to encode or consolidate neglects broader rational considerations about what sorts of associations should be formed at encoding, and which should be retrieved later. Although neurobiological investigations have provided many mechanistic clues about how emotional arousal modulates item memory, these effects have not been wholly integrated with the cognitive and computational neuroscience of memory more generally. Here we apply the Context Maintenance and Retrieval Model (CMR; Polyn, Norman, & Kahana, 2009) to this problem by extending it to describe the way people may represent and process emotional information. A number of ways to operationalize the effect of emotion were tested. The winning emotional CMR (eCMR) model conceptualizes emotional memory effects as arising from the modulation of a process by which memories become bound to ever-changing temporal and emotional contexts. eCMR provides a good qualitative fit for the emotional list-composition effect and the emotional oddball effect, illuminating how these effects are jointly determined by the interplay of encoding and retrieval processes. eCMR can account for the increased advantage of emotional memories in delayed memory tests by assuming a limited ability to reinstate the temporal context of encoding after a delay. By leveraging the rich tradition of temporal context models, eCMR helps integrate existing effects of emotion and provides a powerful tool to test mechanisms by which emotion affects memory in a broad range of paradigms. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

LDPC codes for compression of multi-terminal sources with hidden Markov correlation

Abstract: We propose the use of low-density parity-check (LDPC) codes for compression of memory correlated binary sources. The correlation model between the sources is defined by a hidden Markov model. The resulting performance is close to the theoretical Slepian-Wolf (1973) limit. No information about the sources statistics is required in the encoding process.

Image encoding and decoding apparatus

Abstract: An image encoding apparatus, image decoding apparatus, or image encoding and decoding apparatus having a circuit for dividing a screen into sub-areas to conduct an encoding or decoding operation for each sub-area includes an image dividing circuit for dividing an input image into sub-areas each having a predetermined contour, sub-encoders for encoding the sub-areas respectively related thereto, a code integrating circuit for integrating the codes of the respective sub-areas, and a shared memory for storing images locally decoded by the sub-encoders.