Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

Abstract

Neural activity during ripples has attracted great theoretical and experimental attention over the last three decades. Perhaps one reason for such interest is that ripples occur during quiet waking moments and during sleep, times when we reflect and dream about what has just occurred and what we expect to happen next. The hope is that understanding such ‘offline’ activity may yield insights into reflection, planning, and the purposes of sleep. This review focuses on the mechanisms by which neurons organize during these high-frequency events. In studying ripples, broader principles have emerged that relate intrinsic neural properties, network topology and synaptic plasticity in controlling neural activity. Ripples, therefore, serve as an excellent model for studying how properties of a neural network relate to neural dynamics. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

Multiplexed Surface Electrode Arrays Based on Metal Oxide Thin‐Film Electronics for High‐Resolution Cortical Mapping

“…Specifically, electrodes that presented activity in the ripple band were centered around midline structures and the right retrosplenial cortex, covering an area of ≈ 2 × 1.5 mm 2 of cortical surface (Figure 5f). These observations are in line with previous work, where cortical oscillations with peak frequencies ranging from 100 to 150 Hz have been reported in different cortical areas, [38] with prevalence in associational areas, such as the posterior parietal cortex and midline structures, such as cingulate and retrosplenial cortices. [34] Focusing on the physiological features of observed cortical ripples, they presented a median duration of 96 ± 58 ms (Figure S15d, Supporting Information), a mean peak frequency of 134 ± 9 Hz (Figure S15e, Supporting Information), and an occurrence of 0.75 events per second (n = 97 ripples from 129 s of recording in 1 mouse).…”

Memories replayed: reactivating past successes and new dilemmas

“…(d) Finally, dimensionality reduction techniques such as principle component analysis (PCA) can also be used to identify cell groups and then be used to track the cell group activity across time (for example, see [26,27]). For examples of unsupervised-learning machine learning techniques that are used for memory reactivation analysis please see [17] issue [18,20,21,36,[45][46][47][48][49]). Yet at present, the information being processed during reactivation can at best be deduced based upon the subsequent offline changes in a memory.…”

“…issue [18,20,21,36,[45][46][47][48][49]). Yet at present, the information being processed during reactivation can at best be deduced based upon the subsequent offline changes in a memory.…”

Temporal association between sleep spindles and ripples in the human anterior and mediodorsal thalamus

“…It was proposed that NREM sleep‐based memory formation depends on the hierarchical nesting of slow waves, sleep spindles and hippocampal ripples, as these oscillations are instrumental in transferring information from the hippocampus to the neocortex (Diekelmann & Born, 2010; Staresina et al, 2015). In the past few years, accumulating evidence was found for the existence of ripples outside of the hippocampus (for a review, see McKenzie et al, 2020). Ripples were detected in several cortical areas, such as somatosensory and motor cortices (Averkin et al, 2016), olfactory cortices (Manabe et al, 2011), parahippocampal regions (Axmacher et al, 2008), and higher‐order associational cortices as well (Khodagholy et al, 2017).…”