Localizing spontaneous memory reprocessing during human sleep

Reactivation of newly acquired memories during sleep across hippocampal and neocortical systems is proposed to underlie systems memory consolidation. Here, we investigate spontaneous memory reprocessing during sleep by applying machine learning to source space-transformed magnetoencephalographic data in a two-step exploratory and confirmatory study design. We decode memory-related activity from slow oscillations in hippocampus, frontal cortex and precuneus, indicating parallel memory processing during sleep. Moreover, we show complementary roles of hippocampus and neocortex: while gamma activity indicated memory reprocessing in hippocampus, delta and theta frequencies allowed decoding of memory in neocortex. Neocortex and hippocampus were linked through coherent activity and modulation of high-frequency gamma oscillations by theta, a dynamic similar to memory processing during wakefulness. Overall, we noninvasively demonstrate localized, coordinated memory reprocessing in human sleep.

The effects of spatial stability and cue type on spatial learning: Implications for theories of parallel memory systems

Some theories of spatial learning predict that associative rules apply under only limited circumstances. For example, learning based on a boundary has been claimed to be immune to cue competition effects because boundary information is the basis for the formation of a cognitive map, whilst landmark learning does not involve cognitive mapping. This is referred to as the cue type hypothesis. However, it has also been claimed that cue stability is a prerequisite for the formation of a cognitive map, meaning that whichever cue type was perceived as stable would enter a cognitive map and thus be immune to cue competition, while unstable cues will be subject to cue competition, regardless of cue type. In experiments 1 and 2 we manipulated the stability of boundary and landmark cues when learning the location of two hidden goals. One goal location was constant with respect to the boundary, and the other constant with respect to the landmark cues. For both cue types, the presence of distal orientation cues provided directional information. For half the participants the landmark cues were unstable relative to the boundary and orientation cues, whereas for the remainder of the participants the boundary was unstable relative to landmarks and orientation cues. In a second stage of training, all cues remained stable so that both goal locations could be learned with respect to both landmark and boundary information. According to the cue type hypothesis, boundary information should block learning about landmarks regardless of cue stability. According to the cue stability hypothesis, however, landmarks should block learning about the boundary when the landmarks appear stable relative to the boundary. Regardless of cue type or stability the results showed reciprocal blocking, contrary to both formulations of incidental cognitive mapping. Experiment 3 established that the results of Experiments 1 and 2 could not be explained in terms of difficulty in learning certain locations with respect to different cue types. In a final experiment, following training in which both landmarks and boundary cues signalled two goal locations, a new goal location was established with respect to the landmark cues, before testing with the boundary, which had never been used to define the new goal location. The results of this novel test of the interaction between boundary and landmark cues indicated that new learning with respect to the landmark had a profound effect on navigation with respect to the boundary, counter to the predictions of incidental cognitive mapping of boundaries.

The Quantified Self

This chapter presents an account of sousveillance and how we produce, monitor, and react to data relating to ourselves, thus creating a quantified self. The term sousveillance refers to the personal monitoring and management of one's life through self-generated data. Continuously tracking personal data via sensors and cameras, and recording and analyzing them, would allow a person to manage and memorialize everyday life. The key technological idea that research teams, companies and artists were exploring at the time was lifelogs. The ultimate aim is the simultaneous digitization of all cognitive inputs experienced by the brain via all five human senses to create a digital parallel memory of the lived experiences of a person. However, lifelogs raise a series of ethical and legal questions that were largely being bypassed. Lifelogs would make an authoritarian, Big Brother society easier to put in place and more difficult to overthrow. At the same time as people became interested in sousveillance and lifelogs, so-called 'intimate technologies' — that is, digital tech that are in service to the individual, such as smartphones and wearable computing — started to grow enormously in popularity. These were complemented, and supported, by what might be termed 'scopophilic technologies', that is, digital tech that enable pleasure in looking and being looked at.

Memory Engrams in the Neocortex

While in the past much of our knowledge about memory representations in the brain has relied on loss-of-function studies in which whole brain regions were temporarily inactivated or permanently lesioned, the recent development of new methods has ushered in a new era of downright “engram excitement.” Animal research is now able to specifically label, track, and manipulate engram cells in the brain. While early studies have mostly focused on single brain regions like the hippocampus, recently more and more evidence for brain-wide distributed engram networks is emerging. Memory research in humans has also picked up pace, fueled by promising magnetic resonance imaging (MRI)-based methods like diffusion-weighted MRI (DW-MRI) and brain decoding. In this review, we will outline recent advancements in engram research, with a focus on human data and neocortical representations. We will illustrate the available noninvasive methods for the detection of engrams in different neocortical regions like the medial prefrontal cortex and the posterior parietal cortex and discuss evidence for systems consolidation and parallel memory encoding. Finally, we will explore how reactivation and prior knowledge can lead to and enhance engram formation in the neocortex.

Novel Implementation Approach with Enhanced Memory Access Performance of MGS Algorithm for VLIW Architecture

Modified Gram–Schmidt (MGS) algorithm is one of the most-known forms of QR decomposition (QRD) algorithms. It has been used in many signal and image processing applications to solve least square problem and linear equations or to invert matrices. However, QRD is well-thought-out as a computationally expensive technique, and its sequential implementation fails to meet the requirements of many real-time applications. In this paper, we suggest a new parallel version of MGS algorithm that uses VLIW (Very Long Instruction Word) resources in an efficient way to get more performance. The presented parallel MGS is based on compact VLIW kernels that have been designed for each algorithm step taking into account architectural and algorithmic constraints. Based on instruction scheduling and software pipelining techniques, the proposed kernels exploit efficiently data, instruction and loop levels parallelism. Additionally, cache memory properties were used efficiently to enhance parallel memory access and to avoid cache misses. The robustness, accuracy and rapidity of the introduced parallel MGS implementation on VLIW enhance significantly the performance of systems under severe rea-time and low power constraints. Experimental results show great improvements over the optimized vendor QRD implementation and the state of art.