Part 3:What Is Neural Codes Of Memory During Sleep?
Mar 10, 2022
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Brain rhythms in sleep
Slow oscillation (0.5–1 Hz)
During SWS, neocortical activity displays synchronized slow waves between 0.5 and 1 Hz, which are associated with alternation between widespread hyperpolarization and reduced neuronal firing during the DOWN state, and UP states which are associated with widespread depolarization and increased neuronal firing. The cortical slow oscillations also reach and impact hippocampal and thalamic circuits.
Delta wave (1–4 Hz)
High amplitude brain wave with a frequency of oscillation between 1 and 4 Hz. It is prominent during SWS.
Theta oscillation (4–9 Hz)
During REM sleep, the rodent hippocampus exhibits theta oscillations similar to those seen during wakeful exploration.
Spindle oscillation (9–15 Hz)
During SWS, the thalamus and neocortex exhibit brief bursts of EEG oscillations between 9 and 15 Hz, typically lasting 0.5–2 seconds. Sleep spindles often occur in the neocortical UP state and are temporally aligned with hippocampal ripples.
Gamma oscillation (35–120 Hz)
During SWS, human and rodent EEG recordings show gamma oscillations in low (35–50 Hz) and high (60–120 Hz) frequency bands.
Hippocampal sharp wave-ripples (SWRs, 150–300 Hz)
The SWR complex consists of large amplitude sharp waves in the hippocampal LFP and associated fast LFP oscillatory activity filtered between 150 and 300 Hz, typically lasting 50–100 milliseconds. Bursts of SWRs may last up to 400 milliseconds.
Box 2
Methods for analyzing sleep-associated spike activity
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Correlation Analysis
Correlation analysis computes the strength of Pearson correlation between two neurons based on their firing activities in WAKE and SLEEP; the strength of zero-lag, co-activation of pairwise cell firing determines the similarity between neural firing patterns in WAKE and SLEEP [9]. The “explained variance” method assesses how much additional variance in post-SLEEP correlation can be explained by values in WAKE while taking into consideration of pre-SLEEP structure [11].
Template Matching
Template matching compares two spike count matrices (arranged in cell-by-time) that are temporally binned and smoothed [12,31,42], and assesses whether the reactivation in pairwise activity is coherent across neuronal ensembles. The outcome of template matching is sensitive to temporal bin size, and its correlation strength varies between different compressed timescales.
Sequence Matching
Sequence matching is a combinatorial method for examining sequential firing patterns of population spike activity. It computes the match probability by converting neuronal firing orders into a word, compares the match probability between two words (one in WAKE and the other in SLEEP), and determines the statistical significance of the match [26,32]. The sequence matching method is sensitive to spike timing (and consequently to spike detection and sorting) and the number of activated cells in SLEEP.
Principal Component Analysis and Independent Component Analysis
Principal component analysis (PCA) extends the correlation method and assesses the similarity between two correlation matrices between WAKE and SLEEP [43,58]. It computes the reactivation strength between two templates and provides an instant-by-instant resemblance measure between WAKE and SLEEP. A large value of reactivation strength indicates a good similarity (Fig. 4a). However, the reactivation strength is positively correlated with the neuronal firing rate and does not directly reveal the memory content of ensemble firing patterns. The PCA method assumes that the correlation statistic is stationary within both WAKE and SLEEP, which is the strongest limitation in the presence of nonstationary neuronal spiking data. Independent component analysis (ICA) extends the PCA method and finds a linear projection space that separates statistically independent sources. The ICA method is conceptually similar to the PCA method except that there is an additional ICA step followed by PCA [59]. Both PCA and ICA belong to the linear subspace method, therefore they cannot capture any nonlinear transformation, and their reactivation strengths are positively correlated with the quadratic power of temporal firing rate per se.
Topology Analysis
An algebraic topology is a mathematical tool that was borrowed to study hippocampal neuronal coding for spatial topology [60–62]. It is aimed to compute abstract topological properties from the derived topological object and use those to derive a group relationship within neurons.
Population Decoding
Population decoding is a computational approach that uses statistics or information theory to extract quantitative information from neural ensemble spike activity [63]. The population-decoding approach makes certain statistical assumptions about the population spike activity (e.g., independent Poisson assumption) and employs likelihood or Bayesian inference to decode the content of population codes. One class of decoding approach is supervised, which requires the receptive field information about individual neurons [64,65]; another class of decoding approach is unsupervised, which requires no receptive field or behavior measure [66–68] (Fig. 4b). Systematic comparisons ofthese two types of population-decoding methods in a sleep-associated hippocampal memory study are reported in [69].
Box 2 FIGURE I. Unbiased Assessment of Sleep-Associated Neuronal Population Codes
(a) Principal component analysis (PCA) for computing the similarity of two templates of correlation matrices from population spike counts (WAKE and SLEEP) and assessing the reactivation strength during sleep (reproduced with permission, [43]). In WAKE, {λ,1,p1} are associated with the dominant principal component (PC) extracted from PCA. In SLEEP, time-varying reactivation strength is computed. (b) Unsupervised population decoding using a finite-state hidden Markov model (HMM). Specifically, the spatial environment is represented by a finite discrete state space. Trajectories across spatial locations (“states”) are associated with consistent hippocampal ensemble spike patterns, which are characterized by a state transition matrix. From the state transition matrix, a topology graph that defines the connectivity in the state space is inferred [69]. In these two methods, no assumption is made about neuronal RF, and the bin size in Post- SLEEP is independent of the bin size used in WAKE. Since the order ofWAKE and SLEEP can be switched, one can apply these methods to SLEEP data first and then examine their meanings in the WAKE behavior; therefore they both fall into the new paradigm (“memory first, meaning later”).
Trends Box
The thalamus (a subcortical structure) plays an important role in sensory gating,
arousal regulation, and generating thalamocortical sleep spindles. To fully dissect
sleep-associated memory, it is critical to understand three-way communications
among hippocampal-neocortical, thalamocortical, cortico-thalamic circuits in sleep.
Combining electrophysiology, imaging, virtual reality, and optogenetics in
experimental investigations can significantly expand our understanding of neural
codes underlying memory and sleep.
Optogenetics has proven powerful to test the causal role of neural circuits in
memory consolidation and is valuable to creating false memories. Finding effective
means for consolidating false memories may have a significant impact on future
behavior.
Bridging the research gaps between rodents and non-human/human primates in
sleep studies are the key to dissecting circuit mechanisms in consolidating various
forms of memories, and to provide further insights into the treatment of neurological
and psychiatric diseases.
Outstanding Questions
WHAT: representation—the content of sleep-associated memory in hippocampal- neocortical network. Does sleep-associated spike activity have any significant representation and how to assess their significance? Does the content of sleep-associated memory in one brain region help decipher the content of sleep-associated memory in another region?
WHEN: temporal coordination—the timing of memory reactivation (e.g., coincident or non-coincident ripple and spindle events) and their distinct functional roles. How does hippocampal-neocortical coordination evolve in different sleep stages?
WHERE: Episodic memories consist of spatiotemporal sequences in behavioral experiences, including spatial trajectory coding and non-spatial sequence coding. How can we distinguish the content of spatial vs. non-spatial memories in sleep? Can we read out contextual or emotional memories in sleep? To what extent can we identify the content of hippocampal-neocortical population codes during REM sleep? What’s the principled way to systematically investigate creativity and insights in sleep? Do the NREM and REM sleep play different roles in consolidating declarative memory versus procedure memory? What are the circuit mechanisms that allow external factors (e.g., reward, sensory cue) to bias the content of sleep-associated memory? Are they top-down or bottom-up? How can we effectively manipulate sleep-associated memory to improve the performance of post-sleep cognitive functions? Are false memories consolidated in the same way as true memories during sleep? What are the effective ways to enhance or suppress them? Can investigations of sleep-associated memory reveal new discoveries between normal and aging/diseased brains, or even between ordinary and genius brains?
FIGURE 1. Study ofRodent Hippocampal Memory and Sleep
(a) A standard study paradigm for rodent hippocampal memory consists of pre-RUN sleep, RUN/behavior, and post-RUN sleep. (b) Classification of sleep stages from EMG, cortical LFP (Delta power), hippocampal ripple power, and cortical theta/delta power ratio [21]. (c) Rodent hippocampal population spike activity during RUN on a linear track. (d) Rodent hippocampal LFP and SWRs during post-RUN SWS, and the associated spatiotemporal spike pattern that shows a similar temporal order (“replay”) (reproduced with permission [18]).
FIGURE 2. Dissection of Hippocampal-Neocortical Memories during Sleep
(a,b) Neuronal firing sequences in rat V1 (a) and hippocampus (b) during RUN and POST- RUN SWS episodes. Lap: population neuronal firing pattern during a single running lap on the left-to-right trajectory. Each row represents a cell and each tick represents a spike. Avg: template firing sequence obtained by averaging over all laps on the trajectory. Each curve represents the average firing rate of a cell. Cells were assigned to numbers 0, 1, etc. and then arranged (01234567) from bottom to top according to the order of their firing peaks (vertical lines). Frame:the same population firing patterns in a POST-RUN SWS episode. Triangles and circles denote the onset of and DOWN states, respectively. Seq: firing sequence in the frame. Spike trains were convolved with a Gaussian window and cells were ordered (0132567) according to the peaks (vertical lines) ofthe resulted curves [36]. (c) Auditory sound (L, in red, indicating a left turn) biased the hippocampal reactivation during SWS [37]. In the raster plot, spikes from place cells with place fields on the right side ofthe track are blue, and left-sided place fields in red. Place fields are ordered from top to bottom by their location on the track (right-left side). Prior to sleep onset, the rat was resting in the sleep chamber. The reactivation event in the green dashed box is shown to the right. (d) Sound-biased auditory cortical neuronal ensembles (green) predict reactivations of hippocampal neurons (orange) during SWRs. Pink bars indicate sounds; cyan bars indicate detected SWRs. Top black trace is ripple-filtered LFP in hippocampal CA1 [38]. (e) Quantification of prediction gain of using sound-based pre-SWR auditory cortical (AC) ensemble spike patterns to predict hippocampal CA1 firing. Data is significantly different from the shuffled statistics (n=96) [38]. All figures are reproduced with permission.
FIGURE 3. Decoding the Content of visual Imagery during Human REM Sleep
(a) fMRI data were acquired from sleeping participants simultaneously with polysomnography. Participants were awakened during sleep stage 1 or 2 (red dashed line) and verbally reported their visual experience during sleep. The fMRI data immediately before awakening (9 s) were used as the input for main decoding analyses (sliding time windows were used for time-course analyses). Words describing visual objects or scenes (red letters) were extracted. The visual contents were predicted using machine-learning decoders trained on fMRI responses to natural images. (b) During the training phase, words describing visual objects or scenes were first mapped onto synsets of the WordNet tree [a dictionary of nouns, verbs, adverbs, adjectives, and their lexical relations]. Synsets were grouped into “base synsets” located higher in the tree. Visual reports (participant 2) are represented by visual content vectors, in which the presence or absence of the base synsets in the report at each awakening is indicated by white or black, respectively. Examples of images used for decoder training are shown for some of the base synsets. During the testing phase, a pairwise or multi-label decoder is applied to the awakening event for predicting the visual object label (reproduced with permission [56]).
