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.

Predictive, interactive multiple memory systems.

Abstract: Most lesion studies in animals, and neuropsychological and functional neuroimaging studies in humans, have focused on finding dissociations between the functions of different brain regions, for example in relation to different types of memory. While some of these dissociations can be questioned, particularly in the case of neuroimaging data, we start by assuming a "modal model" in which at least three different memory systems are distinguished: an episodic system (which stores associations between items and spatial/temporal contexts, and which is supported primarily by the hippocampus); a semantic system (which extracts combinations of perceptual features that define items, and which is supported primarily by anterior temporal cortex); and modality-specific perceptual systems (which represent the sensory features extracted from a stimulus, and which are supported by higher sensory cortices). In most situations however, behavior is determined by interactions between these systems. These interactions reflect the flow of information in both "forward" and "backward" directions between memory systems, where backward connections transmit predictions about the current item/features based on the current context/item. Importantly, it is the resulting "prediction error"--the difference between these predictions and the forward transmission of sensory evidence--that drives memory encoding and retrieval. We describe how this "predictive interactive multiple memory systems" (PIMMS) framework can be applied to human neuroimaging data acquired during encoding or retrieval phases of the recognition memory paradigm. Our novel emphasis is thus on associations rather than dissociations between activity measured in key brain regions; in particular, we propose that measuring the functional coupling between brain regions will help understand how these memory systems interact to guide behavior.

Error correction coding over multiple memory pages

Abstract: A method for data storage includes encoding each of multiple data items individually using a first Error Correction Code (ECC) to produce respective encoded data items. The encoded data items are stored in a memory. The multiple data items are encoded jointly using a second ECC, so as to produce a code word of the second ECC, and only a part of the code word is stored in the memory. The stored encoded data items are recalled from the memory and the first ECC is decoded in order to reconstruct the data items. Upon a failure to reconstruct a given data item from a respective given encoded data item by decoding the first ECC, the given data item is reconstructed based on the part of the code word of the second ECC and on the encoded data items other than the given encoded data item.

Software-implemented EDAC protection against SEUs

Abstract: In many computer systems, the contents of memory are protected by an error detection and correction (EDAC) code Bit-flips caused by single event upsets (SEU) are a well-known problem in memory chips; EDAC codes have been an effective solution to this problem These codes are usually implemented in hardware using extra memory bits and encoding/decoding circuitry In systems where EDAC hardware is not available, the reliability of the system can be improved by providing protection through software Codes and techniques that can be used for software implementation of EDAC are discussed and compared The implementation requirements and issues are discussed, and some solutions are presented The paper discusses in detail how system-level and chip-level structures relate to multiple error correction A simple solution is presented to make the EDAC scheme independent of these structures The technique in this paper was implemented and used effectively in an actual space experiment We have observed that SEU corrupt the operating system or programs of a computer system that does not have any EDAC for memory, forcing the system to be reset frequently Protecting the entire memory (code and data) might not be practical in software However this paper demonstrates that software-implemented EDAC is a low-cost solution that provides protection for code segments and can appreciably enhance the system availability in a low-radiation space environment

Facing the future: memory as an evolved system for planning future acts.

Abstract: All organisms capable of long-term memory are necessarily oriented toward the future. We propose that one of the most important adaptive functions of long-term episodic memory is to store information about the past in the service of planning for the personal future. Because a system should have especially efficient performance when engaged in a task that makes maximal use of its evolved machinery, we predicted that future-oriented planning would result in especially good memory relative to other memory tasks. We tested recall performance of a word list, using encoding tasks with different temporal perspectives (e.g., past, future) but a similar context. Consistent with our hypothesis, future-oriented encoding produced superior recall. We discuss these findings in light of their implications for the thesis that memory evolved to enable its possessor to anticipate and respond to future contingencies that cannot be known with certainty.

An Information-Theoretic Approach to Distributed Compressed Sensing ∗

Abstract: Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a concept that we term the joint sparsity of a signal ensemble. We study a model for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the Slepian-Wolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.