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Comparison of repeating spike sequences in a parallel spike train, recorded during wheel running, with its shuffled surrogates. a, Peaks of oscillation were taken as a reference point, and the spike timing was converted to phase values within the cycle. During shuffling, sets of spikes within a given cycle were transposed randomly (arrows). b, Phase-normalized spike density histograms during the cycle are shown. c, Cross-correlogram between the negative peaks of local and unit discharges is shown. d, Spike autocorrelograms of units are shown. Note the similar spike dynamics in the original and shuffled spike trains. e, Repetition curves of spike sequences in the original spike train and in its shuffled surrogates are shown.
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Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A templ...
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... shuffling. This procedure preserved the periodic mod- ulation of discharge frequency both within and across the spike trains (see Fig. 5a). First, the peaks of the field waves were identified. Second, the spike times were converted to phases of the cycle (C sicsvari et al., 1999). Third, the phase-encoded spikes within a given cycle were exchanged with other pseudorandomly selected cycles within the same spike ...
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... somewhat by this method, the field modulation of the neurons is better preserved. Panel d, Shuffling of spikes across spike trains. This method preserves population modula- tion of spike timing but may reduce firing-rate differences between the original spike trains. A fourth method (phase-invariant spike shuffling) is illustrated below (see Fig. ...
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... number of repeating sequences extracted from the original spike train exceeded the number of repeating sequences present in each of the 100 surro- gate trains. Comparison between the original spike train and its phase-corrected shuffled surrogates (see phase-invariant shuf- fling in Materials and Methods) is illustrated in Figure 5. The phase-corrected shuffling procedure preserved the phase relation- ship between and the individual spikes, therefore reproducing the population dynamics of the parallel spike train as revealed by the identical phase-locked modulation and the similar cross- correlograms of both original and shuffled spikes (Fig. 5b,c). ...
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... Methods) is illustrated in Figure 5. The phase-corrected shuffling procedure preserved the phase relation- ship between and the individual spikes, therefore reproducing the population dynamics of the parallel spike train as revealed by the identical phase-locked modulation and the similar cross- correlograms of both original and shuffled spikes (Fig. 5b,c). This procedure also preserved the within-spike-train dynamics of sin- gle neurons, as indicated by the similar autocorrelograms of the original and shuffled spike trains (Fig. 5d). Comparison of repeat- ing spike sequences indicated that the number of repeating spike sequences (r) was less for all sequences (m) in any of the 133 ...
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... dynamics of the parallel spike train as revealed by the identical phase-locked modulation and the similar cross- correlograms of both original and shuffled spikes (Fig. 5b,c). This procedure also preserved the within-spike-train dynamics of sin- gle neurons, as indicated by the similar autocorrelograms of the original and shuffled spike trains (Fig. 5d). Comparison of repeat- ing spike sequences indicated that the number of repeating spike sequences (r) was less for all sequences (m) in any of the 133 shuffled surrogates compared with the original spike train (Fig. 5e). Of the various shuffling methods, across-spike-train shuffling resulted in the most spike sequence repetitions; ...
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... dynamics of sin- gle neurons, as indicated by the similar autocorrelograms of the original and shuffled spike trains (Fig. 5d). Comparison of repeat- ing spike sequences indicated that the number of repeating spike sequences (r) was less for all sequences (m) in any of the 133 shuffled surrogates compared with the original spike train (Fig. 5e). Of the various shuffling methods, across-spike-train shuffling resulted in the most spike sequence repetitions; therefore it may be regarded as the most rigorous test. Figure 6 illustrates the difference between repeating spike sequences obtained from the original parallel spike trains recorded from five rats and the Monte Carlo ...
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The hippocampus provides a spatial map of the environment. Changes in the environment alter the firing patterns of hippocampal neurons, but are presumably constrained by elements of the network dynamics. We compared the neural activity in CA1 and CA3 regions of the hippocampus in rats running for water reward on a linear track, before and after the...
Citations
... Even though dreaming is a universal phenomenon, characterizing its role is still, up to this day, a challenging task. For instance, it is difficult to assess whether the improvement of skills after a night of sleep is due to the occurrence of a certain dream, or to other physiological features of sleep such as hippocampal replay during NREM sleep [25]. In other words, the potential effects of sleeping and dreaming are entangled and thus confounded, since they are naturally co-occurring. ...
The importance of sleep for healthy brain function is widely acknowledged. However, it remains unclear how the internal generation of dreams might facilitate cognitive processes. In this perspective, we review a computational approach inspired by artificial intelligence that proposes a framework for how dreams occurring during rapid-eye-movement (REM) sleep can contribute to learning and creativity. In this framework, REM dreams are characterized by an adversarial process that, against the dream reality, tells a discriminator network to classify the internally created sensory activity as real. Such an adversarial dreaming process is shown to facilitate the emergence of real-world semantic representations in higher cortical areas. We further discuss the potential contributions of adversarial dreaming beyond learning, such as balancing fantastic and realistic dream elements and facilitating the occurrence of creative insights. We characterize non-REM (NREM) dreams, where a single hippocampal memory is replayed at a time, as serving the complementary role of improving the robustness of cortical representations to environmental perturbations. We finally explain how subjects can become aware of the adversarial REM dreams, but less of the NREM dreams, and how content- and state-awareness in wake, dream, and lucid dreaming may appear.
... Even though dreaming is a universal phenomenon, characterizing its role is still, up to this day, a challenging task. For instance, it is difficult to assess whether the improvement of skills after a night of sleep is due to the occurrence of a certain dream, or to other physiological features of sleep such as hippocampal replay during NREM sleep [27]. In other words, the potential effects of sleeping and dreaming are entangled and thus confounded, since they are naturally co-occurring. ...
The importance of sleep for healthy brain function is widely acknowledged. However, it remains unclear how the internal generation of dreams might facilitate cognitive processes. In this perspective we review a computational approach inspired by artificial intelligence that proposes a framework for how dreams occurring during rapid-eye-movement (REM) sleep can contribute to learning and creativity. In this framework, REM dreams are characterized by an adversarial process that, against the dream reality, tell a discriminator network to classify the internally created sensory activity as real. Such an adversarial dreaming process is shown to facilitate the emergence of real-world semantic representations in higher cortical areas. We further discuss the potential contributions of adversarial dreaming beyond learning, such as balancing fantastic and realistic dream elements, and facilitating the occurrence of creative insights. We characterize non-REM (NREM) dreams, where a single hippocampal memory is replayed at a time, as serving a complementary role of improving the robustness of cortical representations to environmental perturbations. We finally explain how subjects can become aware of the adversarial REM dreams, but less of the NREM dreams, and how the awareness phenomenon of wake, dream and lucid dreaming may appear.
... The slope of the fitted line was used to distinguish between a forward trajectory, when the decoded path followed the direction of running during the task, and a reverse trajectory, when it ran in the opposite direction. Hippocampal replay and theta sequences typically involve timecompressed representations (Gupta et al., 2012;Nádasdy et al., 1999). We estimated the compression factor as the speed of the trajectory divided by the average running speed of the animal on the maze arms in the corresponding session, and we found an average compression factor of 5.8 (across all task phases together, median and interquartile range: 3.8 [2.5, 7.1]). ...
... We estimated the compression factor as the speed of the trajectory divided by the average running speed of the animal on the maze arms in the corresponding session, and we found an average compression factor of 5.8 (across all task phases together, median and interquartile range: 3.8 [2.5, 7.1]). This is of the same order of magnitude as previously reported for CA1 during sleep (Ji and Wilson, 2007;Lansink et al., 2009;Lee and Wilson, 2002;Nádasdy et al., 1999) or during theta sequences in the awake state (Dragoi and Buzsáki, 2006;Maurer et al., 2012;Pezzulo et al., 2017). ...
Compressed hippocampal place-cell sequences have been associated with memory storage, retrieval and planning, but it remains unclear how they align with activity in the parahippocampal cortex. In a visuospatial discrimination task, we found a wide repertoire of hippocampal place cell sequences, which recapitulated paths across the task environment. Place cell sequences generated at reward sites predominantly reiterated trajectories near the chosen maze side, whereas trajectories associated with the side chosen in the previous trial were underrepresented. We hypothesized that neurons in the perirhinal cortex, which during the task display broad firing fields correlated with the animal's location, might reactivate in concert with hippocampal sequences. However, we found no evidence of significant perirhinal engagement during virtual trajectories, indicating that these hippocampal memory-related operations can occur independently of the perirhinal cortex.
... The latter firings include replays or preplays of spiking sequences at a rapid timescale, repeated spiking of individual cells, or a combination these activities, which impact the stochasticity scores [69][70][71][72][73][74][75][76]. Indeed, a replay may last between a couple of dozens to a couple of hundreds of milliseconds, which is comparable to the characteristic interval between spikes triggered by the animal's physical passing through place fields [60,[64][65][66][67][68]. These brisk, endogenous network activities clutter the overall spike patterns and drive the β P and λ P scores up (Figs. 8 and M2). ...
Alzheimer’s disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales—spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal’s location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions.
Significance
We expose differences in WT and tau brains, emerging at the circuit level, using a novel, morphological perspective on neural activity. This approach allows identifying qualitative changes in spiking patterns and in extracellular field oscillations, that are not discernible through traditional time-localized or time-averaged analyses. In particular, analyses of activity patterns facilitates detection of neurodegenerative deviations, conspicuously linking their effects to behavior and locomotion, thus opening a new venue for understanding how the architecture of neural activity shifts from normal to pathological.
... Even though dreaming is a universal phenomenon, characterizing its role is still, up to this day, a challenging task. It is, for instance, difficult to assess whether the improvement of skills after a night of sleep is due to the occurrence of a certain dream, or to other physiological features of sleep such as hippocampal replay during NREM sleep (Nadasdy et al., 1999). Nonetheless, there have been some attempts to investigate these effects, notably through the use of pharmacological interventions to suppress REM sleep in participants (see, e.g., Oudiette et al., 2012). ...
The importance of sleep for healthy brain function is widely acknowledged. However, it re- mains mysterious how the sleeping brain, disconnected from the outside world and plunged into the fantastic experiences of dreams, is actively learning. In this perspective article, we review a computational approach inspired by modern artificial intelligence that suggests a role of dreams occurring during rapid-eye-movement (REM) sleep. REM dreams are characterized by an adver- sarial process between feedforward and feedback pathways generating new virtual experiences from the combination of old memories. Such an adversarial dreaming process is shown to facilitate the emergence of semantic, cortical representations. We further discuss the potential contributions of adversarial dreaming beyond learning, such as maintaining a balance between fantasy and reality, and facilitating the occurrence of creative insights. Finally, we characterize non-REM (NREM) dreams, replaying individual memories, that may serve a complementary role by improving the robustness of cortical representations to environmental perturbations.
... While rodent studies have focused on spatial memories, hippocampal neurons can be generally understood to represent the conjunction of the sensory features associated with a particular context (Moser et al., 2015), and the temporal sequences that connect local contexts across time during an experience (Eichenbaum and Cohen, 2014;Eichenbaum, 2017). Importantly, sequential firing patterns of neural ensembles reactivate in a time-compressed manner during some of the population burst events (PBEs) that occur during sharp-wave ripple oscillations in sleep or quiet wakefulness (Wilson and McNaughton, 1994;Skaggs and McNaughton, 1996;Kudrimoti et al., 1999;Nádasdy et al., 1999). By decoding those events, it has been shown the replay trajectories show a continuum of conformity to the original experience, including variability in momentum and both forward and reverse re-expression (Lee and Wilson, 2002;Foster and Wilson, 2006;Diba and Buzsáki, 2007;Csicsvari et al., 2007;Davidson et al., 2009;Krause and Drugowitsch, 2022). ...
Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural state repetition from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson Gaussian-process latent variable model (P-GPLVM) (Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the internal state repetition. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally-compressed patterns of activity, such as those found in population burst events (PBEs) during hippocampal sharp-wave ripples, as well as metrics for assessing whether the inferred new latent variables are congruent with a previously learned manifold in the latent space. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain internal neural states are observed to repeat, which is in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, repetition of internal neural states can be estimated for neural activity during PBEs as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.
... In the absence of salient ongoing sensory stimuli, the brain may instead learn from previous experiences by repeatedly replaying or reactivating neural patterns that were active during past experiences 1,2,[8][9][10] . Such reactivations involve temporally condensed, hypersynchronous events that occur during quiet waking and sleep 1,2,[8][9][10][11] . ...
... In the absence of salient ongoing sensory stimuli, the brain may instead learn from previous experiences by repeatedly replaying or reactivating neural patterns that were active during past experiences 1,2,[8][9][10] . Such reactivations involve temporally condensed, hypersynchronous events that occur during quiet waking and sleep 1,2,[8][9][10][11] . First observed and most commonly studied in the hippocampus, reactivations have also been observed in the amygdala, prefrontal cortex, visual cortex and elsewhere [11][12][13][14][15][16][17][18][19][20][21][22][23] . ...
Many theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the pattern1,2. However, sensory-evoked patterns are not stable but, instead, drift across repeated experiences3–6. Here, to investigate the relationship between reactivations and the drift of sensory representations, we imaged the calcium activity of thousands of excitatory neurons in the mouse lateral visual cortex. During the minute after a visual stimulus, we observed transient, stimulus-specific reactivations, often coupled with hippocampal sharp-wave ripples. Stimulus-specific reactivations were abolished by local cortical silencing during the preceding stimulus. Reactivations early in a session systematically differed from the pattern evoked by the previous stimulus—they were more similar to future stimulus response patterns, thereby predicting both within-day and across-day representational drift. In particular, neurons that participated proportionally more or less in early stimulus reactivations than in stimulus response patterns gradually increased or decreased their future stimulus responses, respectively. Indeed, we could accurately predict future changes in stimulus responses and the separation of responses to distinct stimuli using only the rate and content of reactivations. Thus, reactivations may contribute to a gradual drift and separation in sensory cortical response patterns, thereby enhancing sensory discrimination⁷.
... In rodents, a substantial portion of the variability in cellular activity of hippocampal neurons is explained by space, whereby individual neurons' discharges are correlated to specific locations in a spatial environment-these are known as place cells 14 . Accordingly, reactivated patterns in the medial temporal lobe and medial prefrontal cortex manifest themselves as sequences of trajectories undertaken in a previously explored environment [15][16][17][18][19] . As is the case between cortical regions, the hippocampus reactivates in coordination with many cortical areas [5][6][7]9,20 . ...
... From the output side, it may be inferred from the present results that the information received by cortical sites downstream from the hippocampus during offline reactivation comprises sequential place cells activations analogous to the patterns of replay observed directly in the hippocampus [15][16][17]19 . Similarly, previous reports on coordinated reactivations between the hippocampus and the cortex for the same behavioural experiences have largely involved spatial features [5][6][7]20 . ...
Episodic memories comprise diverse attributes of experience distributed across neocortical areas. The hippocampus is integral to rapidly binding these diffuse representations, as they occur, to be later reinstated. However, the nature of the information exchanged during this hippocampal-cortical dialogue remains poorly understood. A recent study has shown that the secondary motor cortex carries two types of representations: place cell-like activity, which were impaired by hippocampal lesions, and responses tied to visuo-tactile cues, which became more pronounced following hippocampal lesions. Using two-photon Ca²⁺ imaging to record neuronal activities in the secondary motor cortex of male Thy1-GCaMP6s mice, we assessed the cortical retrieval of spatial and non-spatial attributes from previous explorations in a virtual environment. We show that, following navigation, spontaneous resting state reactivations convey varying degrees of spatial (trajectory sequences) and non-spatial (visuo-tactile attributes) information, while reactivations of non-spatial attributes tend to precede reactivations of spatial representations surrounding hippocampal sharp-wave ripples.
... While previous work, mostly in the hippocampus [71][72][73] and cortex [74][75][76] , has suggested a role for neural reactivations in memory [77][78][79] , our study advances this idea in multiple important ways. First, we bring this concept to an entirely new paradigm (postingestive learning and CTA). ...
Animals learn the value of foods based on their postingestive effects and thereby develop aversions to foods that are toxic and preferences to those that are nutritious. However, it remains unclear how the brain is able to assign credit to flavors experienced during a meal with postingestive feedback signals that can arise after a substantial delay. Here, we reveal an unexpected role for postingestive reactivation of neural flavor representations in this temporal credit assignment process. To begin, we leverage the fact that mice learn to associate novel, but not familiar, flavors with delayed gastric malaise signals to investigate how the brain represents flavors that support aversive postingestive learning. Surveying cellular resolution brainwide activation patterns reveals that a network of amygdala regions is unique in being preferentially activated by novel flavors across every stage of the learning process: the initial meal, delayed malaise, and memory retrieval. By combining high-density recordings in the amygdala with optogenetic stimulation of genetically defined hindbrain malaise cells, we find that postingestive malaise signals potently and specifically reactivate amygdalar novel flavor representations from a recent meal. The degree of malaise-driven reactivation of individual neurons predicts strengthening of flavor responses upon memory retrieval, leading to stabilization of the population-level representation of the recently consumed flavor. In contrast, meals without postingestive consequences degrade neural flavor representations as flavors become familiar and safe. Thus, our findings demonstrate that interoceptive reactivation of amygdalar flavor representations provides a neural mechanism to resolve the temporal credit assignment problem inherent to postingestive learning.
... Imagine navigating a new city before bedtime, and then, during sleep, your brain begins to spontaneously recall the route you took throughout the day. This process mirrors the phenomenon known as 'replay', a process initially described in rodents, characterized by the fast, sequential reactivation of past experiences, and tied to hippocampal sharp wave ripples (SWRs) 1,2 . Replay, particularly during rest and sleep, is believed to facilitate hippocampal-cortical dialogue to enhance offline memory consolidation [3][4][5][6] . ...
The consolidation of discrete experiences into a coherent narrative shapes our cognitive map, providing a structured mental representation of our experiences. Neural replay, by fostering crucial hippocampal-cortical dialogue, is thought to be pivotal in this process. However, the brain-wide engagement coinciding with replay bursts remains largely unexplored. In this study, by employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of replay. We find that during mental simulation, the strength of on-task replay, as detected via EEG, correlates with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Intriguingly, increased replay strength also enhances the functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing cognitive map. Furthermore, during the post-learning resting state, we observed a positive association between increased task-related reactivation, hippocampal activity, and augmented connectivity to the entorhinal cortex. Our findings elucidate the neural mechanism of human replay in both time and space, providing novel insights into dynamics of replay and associated brain-wide activation.
