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Computational role of sleep in memory reorganization
Kensuke Yoshida1,2 and Taro Toyoizumi1,2
Abstract

Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models
did not focus on some sleep characteristics. Here, we review
recent theoretical approaches investigating their roles inlearning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, andrapid eye movement (REM) sleep reorganizes the representations of memories. We first review the possibility that slow
waves during NREM sleep contribute to memory selection byusing sequential firing patterns and the existence of up anddown states. Second, we discuss the role of dreaming during
REM sleep in developing neuronal representations. We finallydiscuss how to develop these points further, emphasizing theconnections to experimental neuroscience and machinelearning.
Addresses
1
Laboratory for Neural Computation and Adaptation, RIKEN Center for
Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan2
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
Corresponding author: Toyoizumi, Taro (taro.toyoizumi@riken.jp)

Current Opinion in Neurobiology 2023, 83:102799
This review comes from a themed issue on Computational Neuroscience 2023
Edited by Jeanette Hellgreny Kotaleski and Tatjana Tchumatchenko
For a complete overview see the Issue and the Editorial
Available online 14 October 2023
https://doi.org/10.1016/j.conb.2023.102799
0959-4388/© 2023 The Authors. Published by Elsevier Ltd. This is an
open access article under the CC BY license (http://creativecommons.
org/licenses/by/4.0/).

Introduction
Sleep is an essential physiological state conserved acrossa variety of species, including nematodes, flies, andmammals. Sleep is considered to reorganize memory of
awake experiences into an efficient form [1e3],
inducing consolidation and assimilation of experiences
and inspiring inference from learned relationships[4e7]. Given its evolutionary conservation, sleep is expected to play an essential role in learning that cannot
be achieved during wakefulness.
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Some sleep characteristics have been well exploredtheoretically. During sleep, our brain lacks sensory
inputs but internally generates neural activity.
Mimicking these characteristics, in the Helmholtz machine composed of recognition and generative connections between the input and hidden layers, learning ofrecognition connections is driven during the sleep phaseby neural activity caused by its generative model insteadof real sensory inputs, while learning of generative
connections is driven during the wake phase by sensory
inputs [8,9]. This suggests that the combination of
wakefulness and sleep might contribute to extracting
hidden input structures. Another sleep characteristic isthat firing patterns about awake experiences are
replayed during sleep [10,11]. Mimicking this, more
recent studies suggested that replays, which are notnecessarily created by generative models but aresampled from the memory buffer (storage of previous
inputs), are effective for preventing catastrophic interference and stabilizing learning [12,13]. While thesestudies have demonstrated the utility of memory replays
in artificial machine learning methods, other studies
begin to propose the computational roles of moredetailed sleep characteristics.
Sleep is divided into non-rapid eye movement (NREM)
and rapid eye movement (REM) sleep with different
characteristics. NREM sleep is characterized by slow
waves, low-frequency (0.5e4.0 Hz) components of EEG
and local field potential, which accompany synchronoustransitions of cortical neurons between up states withhigher membrane potential and down states with lower
membrane potential (Figure 1a) [14]. Slow waves havebeen considered to have causal influences on memory
reorganization, especially when they are temporally
coupled with neuronal reactivation of awake experiences[4,10,15e19]. While the role of the homeostatic synaptic regulation during sleep in memory consolidation
has been investigated [20e22], the function of slow
waves has not been sufficiently addressed theoretically.
On the other hand, REM sleep is dominated by highfrequency neuronal activities as with wakefulness but
accompanies dreaming and hallucinatory experiences
created in our brain [23]. The involvement of REM
sleep in associative thinking and creativity has beensuggested mainly in human studies [3,24]. In addition, a
recent study in rodents suggested that the communication from the medial entorhinal cortex to the anterior
cingulate cortex during REM sleep is critical for theCurrent Opinion in Neurobiology 2023, 83:102799

2 Computational Neuroscience 2023

Figure 1

Slow waves might selectively consolidate memory by arranging cortical replays. a. Cortical neurons transit between up and down states synchronouslywith slow waves. During up and down states, the membrane potential of each neuron is higher and lower, respectively. Replays of spike sequences(orange) are promoted, especially during the transition from down to up states (gray shadow). b. Theoretical model in Ref. [26]. Sequential stimulations ina one-dimensional chain of cortical neurons (from ‘A’ to ‘E’) strengthen the synaptic weights in one direction (shown in red) during training, which is further
strengthened during post-learning sleep. c. When two opposing patterns (one from ‘A’ to ‘E’ and one from ‘E’ to ‘A’) are trained, replays during slow waves
allow two sequences to coexist by assigning distinct subsets of neurons (shown in magenta and green) for the two directions [27]. d. Slow waves might
improve computation by learning appropriate poly-synaptic paths. Such effects could be beneficial for finding the shortest path from the start neuron (S) to
the target neuron (T) in a recurrent network (shown in green) [29].

emergence of inference [7]. Therefore, how REM sleep
and dreaming promote learning needs to be theoretically investigated.
In this review, we discuss the possibility that NREM
sleep selectively consolidates memory and, based on it,
REM sleep creates efficient neuronal representations.
First, we consider that two features of slow waves:sequential firing patterns along the propagation of slow
waves and the existence of up and down states may
contribute to the selection for memory consolidation.
Second, we discuss the role of dreaming during REM
sleep in developing efficient representations of memories. Dreaming created by our brain might help to
explore and compare more global features of memory
patterns. Thus, the NREM and REM sleep characteristics may synergistically enhance learning.
Current Opinion in Neurobiology 2023, 83:102799

Slow waves selectively consolidate memory
within low-dimensional neuronal space
The transitions from down to up states of slow waves incortical neurons propagate as a traveling wave, whichevokes sequential firing patterns in neurons (Figure 1a).
This might be involved in memory selection byconsolidating memories that are compatible with lowdimensional waves [25,26]. In the setting of [26],sequential stimulations of neurons in the cortex during
wakefulness form a one-dimensional chain of synaptic
weights that facilitates sequential activity in one direction (Figure 1b). During subsequent sleep, slow waves
promote replays of the learned sequence and further
strengthen these synapses (Figure 1b). Awake-likeasynchronous firing patterns are not as efficient as synchronous slow waves in consolidating the learned
sequence. Coherent waves expedite learning by
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Computational role of sleep Yoshida and Toyoizumi

efficiently promoting stereotypical neural activity patterns. These results suggest that memory can bestrengthened using slow waves. A subsequent study
further suggested the effects of slow waves on protecting against catastrophic forgetting in a similar setup[27]. When the model learns one direction of neuralactivity along layers and its reverse direction using thetypical asymmetric spike-timing-dependent plasticity(STDP) time window, their effects interfere with eachother. However, spontaneous replays toward both directions induced by slow waves allow sequences with
opposing directions to coexist by assigning distinct
subsets of neurons for the two directions (Figure 1c).
Another study also pointed out that replays induced byslow waves could improve visual classification [28].
These studies proposed that slow waves efficiently
select memories to be consolidated by promoting
spontaneous replays compatible with their lowdimensional waves. This could also help to realize replays without remembering all inputs in a memory
buffer, which is important in machine learning [12].
Another model proposed the computational benefits ofslow waves on goal-directed behavior [29]. This study
suggested that the combination of slow waves and areward-modulated update rule of synaptic weights enables neuronal networks to efficiently learn polysynaptic
paths that involve multiple synapses in between(Figure 1d). With awake-like asynchronous neuronal
activity patterns, intact sequences to a distant target
neuron rarely appear. However, with the aid of slow
waves that promote sequential activity spreading to
distant neurons, the reward-modulated synaptic update
rule could induce the strengthening of synaptic weights
along the paths. Further, repetitive activation of neurons
by slow waves enables an efficient search over various
candidate paths and could replace an initial detour pathwith a shortcut path that increases reward by achieving
shorter behavioral latency (Figure 1d). This result implies that slow waves can consolidate memory in a form
that is useful for task solving.
In summary, these models show that slow waves canfacilitate selective memory consolidation by confining
replays within low-dimensional neuronal space. The
restriction to the low-dimensional space might introduce inductive bias for assuring spatial continuity ofneural activity and expediting learning. Although thesemethods have not yet been implemented in practicalmachine learning tasks, they constitute interesting
computational hypotheses about the benefit of sleep
in learning.

Multiple states in slow waves coordinately
reorganize memory
A recent theoretical study proposes that synaptic plasticity depends on states during slow waves [30]. This
study adopted a normative approach based on the idea
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3

that biological systems have evolved to achieve optimality in some aspects [31,32] and especially investigated the possibility that synaptic changes are
optimized for maximizing information transmission between neurons. This idea is theoretically formulated by
the information maximization (infomax) synaptic plasticity that changes synaptic strength for increasing themutual information (quantifying “amount of transmitted information” in information theory) between
presynaptic and postsynaptic spike trains (Figure 2a)[33]. The infomax synaptic plasticity differs in up anddown states of slow waves and also depends on their
spatial wavelength. First, the infomax synaptic plasticity
is biased toward synaptic weakening when the baseline
postsynaptic firing rate is high in up states (Figure 2b).
This effect is because the cost of strengthening a synapse is greater than its benefit for information transmission in the up states where frequent backgroundinputs from many synapses degrade information transmission by each synapse (i.e., at a low signal-to-noise
ratio). Second, the infomax synaptic plasticity predicts
that global up and down states should induce more
synaptic strengthening than local up and down states,
respectively (Figure 2b). This effect is again explainedby the weakening bias of the infomax synaptic plasticity
at higher baseline postsynaptic firing rate. In the
excitatory-inhibitory network model that exhibits slow
waves, surrounding neural activity increases and decreases the baseline firing rate of neurons at the centerin their down states and up states, respectively(Figure 2c). The cortical interaction is excitation
dominated in down states but inhibition dominated inup states because inhibitory neurons are assumed tohave a steeper rise of gain with input than excitatoryneurons. Hence, the activity of the surrounding neurons
suppresses that of central neurons in global up states but
facilitates it in local down states (Figure 2c). This surround suppression property is a reminiscent feature ofstabilized supralinear networks [34e36] that has gained
several experimental supports. This indicates that the
global up and down states have lower firing rates thanthe local up and down states, respectively. Hence, theinfomax synaptic plasticity predicts that synaptic
changes should be positively biased in both up and downstates if surrounding cortical areas take the same state as
the center (i.e., in global up and down states). Indeed,
these predictions are consistent with two recent rodent
experiments [18,37].
Furthermore, integrating synaptic changes in down andup states might exert a delicate selection mechanism formemory consolidation. A recent theoretical study suggested that the formation of cell assemblies (i.e., coactive neuronal subpopulations with strong connections in
between) depends on inhibition strength. It proposedthat a network with dominant excitatory interactions
rapidly forms nonspecific cell assemblies that recruiteven non-stimulated neurons, while that with dominant
Current Opinion in Neurobiology 2023, 83:102799

4 Computational Neuroscience 2023

Figure 2

Model of the state-dependent synaptic plasticity based on information maximization. a. The model suggests that sleep might induce optimal synaptic
changes for maximizing mutual information between presynaptic and postsynaptic spike trains. The infomax plasticity is formulated by increasing mutual
information I under the constraint of synaptic weight cost F. b. The model predicts that baseline firing rates modulate optimal synaptic plasticity. Therefore,
the optimal synaptic plasticity and memory reorganization depend on the up and down states of global and local slow waves. These differences are
consistent with previous experimental findings [18,37]. c. Distinct effects of the surrounding inputs during up and down states in an excitatory-inhibitory
network model. Due to the supralinear transfer functions of neurons, inputs from surrounding populations suppress the excitatory firing rates in the center
during up states (shown as an orange line), which is known as surround suppression, while they facilitate the excitatory firing rates in the center during
down states (shown as a blue line). This property, the so-called stabilized supralinear property, has been experimentally supported [34–36]. As a result,
excitatory firing rates are expected to be higher in local up and down states than global up and down states, respectively. Furthermore, this property
implies the possibility that the externally driven neural activity spreads to surrounding populations during down states while it is restricted to the center
during up states, which might lead to different spatial spreads of cell assemblies.
Panels a and b are modified from Ref. [30].

inhibitory interactions slowly forms more specific ones
that selectively recruit only stimulated neurons [38].
Considering the stabilized supralinear property of theslow-wave model described above [30], similar differences may be observed between the down and up statesof NREM sleepedown states may promote the formation of nonspecific cell assembly rapidly compared withup states. While many experimental studies about
memory reorganization have focused on neuronal activities during up states [18,19,39], a recent experimental
study reported that neuronal activities during downstates are also related to hippocampal ripple activities
and, thus, memory consolidation [40]. Hence, up anddown states of slow waves with distinct synaptic
Current Opinion in Neurobiology 2023, 83:102799

plasticity and excitatory-inhibitory balance may playcomplementary roles in memory consolidation. For
example, up states might reorganize normal memory
with higher specificity, while down states might promote the consolidation of crucial memory, such as lifethreatening events, rapidly with lower specificity. It is
an interesting future research topic to examine the more
specific division of roles in these states.

Learning representations by dreaming
during REM sleep
Dreaming, often accompanied by REM sleep, is apparently not just a replay of past experiences but more acreative experience generated by our brain [41].
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Computational role of sleep Yoshida and Toyoizumi

Although the detailed mechanism and content ofdreaming are unknown, it is expected to be created by
combining our previous experiences. How these imaginations could improve the brain’s computation constitutes an attractive research topic. Previous studies haveproposed that REM sleep might contribute to creatively
discovering hidden associations [3,24]. In this section,
we consider the hypothesis that dreams during REM
sleep promote exploring more global structures of the
input space by generating imaginary combinations ofexperiences to find efficient representations that reflect
a possible relationship between remote memories(Figure 3a). We review neuroscience studies about
dreaming and state-of-the-art representation learning
methods in the following paragraphs to explore potential ties.
One recent study [42] addressed the role of dreams byconstructing a cortical implementation of generative
adversarial networks (GANs), a successful generativemodel in machine learning by training a ‘generator’ tofool a ‘discriminator’ that learns to distinguish fake andreal data [43] (Figure 3b). The model proposes alearning rule of feedforward (from lower to higher) andfeedback (from higher to lower) synaptic connections.
During wakefulness, both feedforward and feedback
pathways learn similarly to the autoencoder [44] bycompressing sensory input into high-level representation and reconstructing the original sensory input basedon the high-level representation. During NREM sleep,
low-level sensory representation is generated from replays by the feedback pathways with added noise, andthe feedforward pathways learn to reconstruct the5original high-level representation. During REM sleep,
which is termed ‘adversarial dreaming,’ feedback pathways convert ‘creative dreams,’ i.e., fused memory, into
low-level sensory representation, and feedforward
pathways process it to judge if it is a dream. The feedforward pathways learn to discriminate correctly(‘discriminator’), and the feedback pathways learn tofool them (‘generator’). The NREM stage adds
robustness to the high-level representation, and the
REM stage improves the object-identity classification of
sensory input. This study implies that the synthesizeddata explored by dreaming during REM sleep could
contribute to forming representations.
Other studies in machine learning also provide hints
about the possible roles of dreaming. It has been pointed
out that negative samples in contrastive learning would
be biologically implemented during sleep [45].
Contrastive learning is another powerful representation
learning method in machine learning [46], based on theidea that similar data (termed ‘positive sample’) shouldbe embedded close to each other, whereas dissimilar
data (termed ‘negative sample’) should not. The
augmented version of the original data and neighboring
epochs in the video frames are often used as positive
samples, and other irrelevant data are often used as
negative samples. Although positive samples are
considered easily realized in the brain by extracting
neighboring frames from our experiences, negative
samples seem relatively difficult to implement.
Dreaming, in which inconsistent episodes are neighboring in time series, might provide combinations ofdata that work as negative samples. Therefore, dreaming

Figure 3

Dreaming might contribute to forming efficient cortical representations. a. Dreaming synthesized during REM sleep might play a role in forming efficientcortical representations. Dreaming might contribute to discovering possible associations and distinctions between memories. b. The model suggests thepossibility that forming cortical representations is affected by ‘adversarial dreaming’ during REM sleep and ‘replay’ during NREM sleep [42]. During REM
sleep, inspired by GANs, the model trains a discriminator (feedforward projection) to distinguish whether it is a dream and a generator (feedback projection) to fool it. During NREM sleep, the feedforward projection is trained to reproduce representations in a higher cortical area under the condition that
feedback transformations are perturbed. During wakefulness, the feedforward and feedback projections are trained to reproduce original inputs provided
in a lower cortical area.
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Current Opinion in Neurobiology 2023, 83:102799

6 Computational Neuroscience 2023

might improve the representation by exploring the
combinations that should be compared. Note that a
recent paper suggested the possibility that contrastive
learning could be realized during NREM sleep in theframework of [42] (see Ref. [47] for details).
In summary, dreaming might contribute to forming
efficient neural information representations by
exploring and comparing more diverse combinations ofexperiences than during NREM sleep. Such representation learning may have commonalities with machinelearning methods [42,43,45], but the range of distortions involved might go beyond typical engineering
setups today. Another potential difference with machinelearning might lie in the alternation of NREM and REM
sleep several times in one night. NREM sleep consolidates some recent memories with slow waves, and REM
sleep may find hidden relationships between thesememories and ostensibly unrelated others during
dreaming. The prior memory selection by NREM sleep
in iteration might help form relationships between
memories, avoiding false relationships.

Future perspective
Recent theoretical models suggest that slow waves
during NREM sleep regulate memory selection, anddreaming during REM sleep form representations of
selected memories. Theoretical models serve as an
excellent guide to testing this hypothesis, bridging the
gap between experimentally observed sleep characteristics and their computational roles. First, it would
become possible to chronically track neuronal representation changes by using recently-developed methods
for recording many neuronal activities at a high temporal
resolution [48]. Such continual recording over sleep
periods would elucidate whether underlying changes inneuronal circuits during NREM and REM sleep are
consistent with our hypothesis with the aid of theoretical models. Second, the potential performance of themethods incorporating sleep characteristics needs to bestudied. Although many sleep characteristics discussed
in this review are only implemented for proof-ofprinciple demonstration, their computational benefits
in more practical tasks should be also addressed in the
future. In summary, cross-disciplinary investigation of
sleep functions will be a key to understanding how thebrain learns and discovering beneficial components for
improving machine-learning methods in the future.

Declaration of competing interest
We have no financial and personal relationships withother people or organizations that could inappropriately
influence the work.

Data availability
No data was used for the research described in
the article.
Current Opinion in Neurobiology 2023, 83:102799

Acknowledgments
This study was supported by RIKEN Center for Brain Science, Brain/
MINDS from AMED under Grant No. JP15dm0207001 (T.T.), KAKENHI
Grant-in-Aid JP18H05432 (T.T.) and JP21J10564 (K.Y.) from JSPS, RIKEN
Junior Research Associate Program (K.Y.), and Masason Foundation (K.Y.).

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