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Both the thalamocortical and limbic systems generate a variety of brain state-dependent rhythms but the relationship between the oscillatory families is not well understood. Transfer of information across structures can be controlled by the offset oscillations. We suggest that slow oscillation of the neocortex, which was discovered by Mircea Steria...
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Memory consolidation is thought to depend on the reactivation of patterns of brain activity that characterize recent experience. Although reactivation has been identified and well-described in the rodent hippocampus, a structure critical for the formation of long-term memories, the persistence of patterns of hippocampal activity has no...
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... In contrast, previous studies have iScience Article proposed that other mechanisms such as adaptation or short term synaptic plasticity that operate in slower time scales, typically several hundreds of milliseconds, underlie the generation of slow rhythms. [68][69][70][71] In the other words, the slow and fast oscillations observed in these studies are generated by distinct mechanisms. Notably, our model lacks such distinct mechanisms with slow time scales and slow spectral components are an emergent dynamical phenomenon resulted from the intermittent appearance of the bursts of the fast oscillations. ...
The brain displays complex dynamics, including collective oscillations, and extensive research has been conducted to understand their generation. However, our understanding of how biological constraints influence these oscillations is incomplete. This study investigates the essential properties of neuronal networks needed to generate oscillations resembling those in the brain. A simple discrete-time model of interconnected excitable elements is developed, capable of closely resembling the complex oscillations observed in biological neural networks. In the model, synaptic connections remain active for a duration exceeding individual neuron activity. We show that the inhibitory synapses must exhibit longer activity than excitatory synapses to produce a diverse range of dynamical states, including biologically plausible oscillations. Upon meeting this condition, the transition between different dynamical states can be controlled by external stochastic input to the neurons. The study provides a comprehensive explanation for the emergence of distinct dynamical states in neural networks based on specific parameters.
... The active system consolidation hypothesis proposes synchronized hippocampal sharp-wave-ripples, thalamocortical sleep spindles and slow oscillations as underlying activities of memory reactivation during sleep. Accordingly, studies in humans [7][8][9] and in rodent models 10,11 found that sleep spindles and hippocampal sharp wave ripples occur grouped during slow oscillations and that these events correlate with memory consolidation 6,[12][13][14][15] . ...
Sleep associated memory consolidation and reactivation play an important role in language acquisition and learning of new words. However, it is unclear to what extent properties of word learning difficulty impact sleep associated memory reactivation. To address this gap, we investigated in twenty-two young healthy adults the effectiveness of auditory targeted memory reactivation (TMR) during non-rapid eye movement sleep of artificial words with easy and difficult to learn phonotactical properties. Here, we found that TMR of the easy words improved their overnight memory performance, whereas TMR of the difficult words had no effect. By comparing EEG activities after TMR presentations, we found an increase in slow wave density independent of word difficulty, whereas the spindle-band power nested during the slow wave up-states - as an assumed underlying activity of memory reactivation - was significantly higher in the easy/effective compared to the difficult/ineffective condition. Our findings indicate that word learning difficulty by phonotactics impacts the effectiveness of TMR and further emphasize the critical role of prior encoding depth in sleep associated memory reactivation.
... Memory consolidation is accomplished by the interplay of sleep slow waves, sharp-wave ripples, hippocampal spindles, and ripples [36]. Slow waves orchestrate this teamwork by placing it under homeostatic regulation [43][44][45][46]. ...
Although a critical link between non-rapid eye movement (NREM) sleep and epilepsy has long been suspected, the interconnecting mechanisms have remained obscure. However, recent advances in sleep research have provided some clues. Sleep homeostatic plasticity is now recognized as an engine of the synaptic economy and a feature of the brain’s ability to adapt to changing demands. This allows epilepsy to be understood as a cost of brain plasticity. On the one hand, plasticity is a force for development, but on the other it opens the possibility of epileptic derailment. Here, we provide a summary of the phenomena that link sleep and epilepsy. The concept of “system epilepsy”, or epilepsy as a network disease, is introduced as a general approach to understanding the major epilepsy syndromes, i.e., epilepsies building upon functional brain networks. We discuss how epileptogenesis results in certain major epilepsies following the derailment of NREM sleep homeostatic plasticity. Post-traumatic epilepsy is presented as a general model for this kind of epileptogenesis.
... In the current study, the two words of a pair were simultaneously presented and the paired-associative encoding in memory occurred around 500 ms following the onset of both words. Because hippocampal paired-associative encoding requires broadly activated neurons for an effective hippocampal-neocortical connectivity (Cox, Korjoukov, et al., 2014 ;Destexhe et al., 2007 ;Schabus et al., 2012 ;Sirota & Buzsáki, 2005 ), a slow-wave peak at the time of associative learning seems mandatory. This time point is during the play of the second word of a pair, when word presentations are sequential, and the time point is around 500 ms following word onset, when word presentations are simultaneous. ...
We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether simultaneously sleep-played foreign words and translation words are encoded/stored and which neural-electrical events facilitate encoding/storage. A closed-loop algorithm targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hours later. These tests required decisions regarding the semantic category of previously sleep-played foreign words. The sleep-played vocabulary influenced awake decision-making 36 hours later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.
... These can be taken as a proxy to investigate mechanisms underlying content-specific representations during encoding and transmitting information through the hippocampal circuitry (Watrous et al., 2015). Consequently, for further progress in understanding the computational principles governing this highly plastic system, it is necessary to examine the electrophysiological features of the network architecture and the dynamics of oscillatory activity in hippocampal-cortical subnetworks at much greater depth and resolution as previously possible (Sirota and Buzsáki, 2005). To satisfy these demands, acute 'brain slices on-chip' represent a very powerful experimental tool for studying neurophysiological processes across scales from cellular to network levels (Huang et al., 2012). ...
Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.
... It is assumed that in such states, the neocortex is in a state of relative sen sory deprivation and is engaged in processing information and signals generated within the brain itself [3]. It has been suggested that the functional role of SO is to support conditions for synchronization of activity of neuronal ensembles located in different parts of the neocortex [6]. However, for synchronization of neuronal activity in spatially separated structures of the neocortex, a general mechanism ensuring synchronization of neuronal activity is required. ...
... The recorded layer of PFC and M1 was confirmed with histology (Extended Data Fig. 1a). We also confirmed that the waveform and spike activity during the detected SOs were comparable to the previous publications 18,20,38,62 . The LFP average across all recording channels excluding bad channels was filtered in the delta band (0.1-4 Hz) through two independent filterings: the high-pass Butterworth filter (second order, zero phase-shifted, with a cutoff at 0.1 Hz) was applied and then followed by the low-pass Butterworth filter (fifth order, zero phase-shifted, with a cutoff at 4 Hz). ...
Systems consolidation—a process for long-term memory stabilization—has been hypothesized to occur in two stages 1–4 . Whereas new memories require the hippocampus 5–9 , they become integrated into cortical networks over time 10–12 , making them independent of the hippocampus. How hippocampal–cortical dialogue precisely evolves during this and how cortical representations change in concert is unknown. Here, we use a skill learning task 13,14 to monitor the dynamics of cross-area coupling during non-rapid eye movement sleep along with changes in primary motor cortex (M1) representational stability. Our results indicate that precise cross-area coupling between hippocampus, prefrontal cortex and M1 can demarcate two distinct stages of processing. We specifically find that each animal demonstrates a sharp increase in prefrontal cortex and M1 sleep slow oscillation coupling with stabilization of performance. This sharp increase then predicts a drop in hippocampal sharp-wave ripple (SWR)–M1 slow oscillation coupling—suggesting feedback to inform hippocampal disengagement and transition to a second stage. Notably, the first stage shows significant increases in hippocampal SWR–M1 slow oscillation coupling in the post-training sleep and is closely associated with rapid learning and variability of the M1 low-dimensional manifold. Strikingly, even after consolidation, inducing new manifold exploration by changing task parameters re-engages hippocampal–M1 coupling. We thus find evidence for dynamic hippocampal–cortical dialogue associated with manifold exploration during learning and adaptation.
... Cortical SOs, thalamo-cortical spindles and sharp wave ripples are thought to be key for memory consolidation, with the up-going SO driving spindle-ripple events with reactivation ( Diekelmann and Born, 2010 ;Sirota and Buzsáki, 2005 ;Khodagholy et al., 2017 ). TMR to the up-going phase of the SO has been shown to improve memory ( Göldi et al., 2019 ;Shimizu et al., 2018 ). ...
Targeted memory reactivation (TMR) is a technique in which sensory cues associated with memories during wake are used to trigger memory reactivation during subsequent sleep. The characteristics of such cued reactivation, and the optimal placement of TMR cues, remain to be determined. We built an EEG classification pipeline that discriminated reactivation of right- and left-handed movements and found that cues which fall on the up-going transition of the slow oscillation (SO) are more likely to elicit a classifiable reactivation. We also used a novel machine learning pipeline to predict the likelihood of eliciting a classifiable reactivation after each TMR cue using the presence of spindles and features of SOs. Finally, we found that reactivations occurred either immediately after the cue or one second later. These findings greatly extend our understanding of memory reactivation and pave the way for development of wearable technologies to efficiently enhance memory through cueing in sleep.
... Homeostatic plasticity of Up-states Up-states have been proposed to have multiple functional roles, including memory consolidation and synaptic homeostasis (Tononi and Cirelli, 2003;Sirota and Buzsáki, 2005;Marshall et al., 2006;Vyazovskiy et al., 2008;Diekelmann and Born, 2010). Consistent with previous studies, our results suggest that Upstates also play a role in the homeostatic regulation of neural activity (Goel and Buonomano, 2013;Motanis and Buonomano, 2015). ...
Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and non-stimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and non-stimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits. And, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT:The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.
... PSI for slow gamma shows an opposite pseudocausality flow, from neocortical activity to hippocampal slow gamma. It is known that hippocampal activity is affected by cortical UP-DOWN state fluctuations (56,57), even at the level of resting membrane potential (58). Here, we suggest that slow gamma oscillations are an important mediator of cortical influences. ...
... This interpretation is consistent with a bidirectional interaction between cortex and hippocampus (Fig. 5) during sleep. An enticing hypothesis, which would have to be explored experimentally and computationally, is that neocortex and hippocampus act in the sleep state as a single network, with transient activations that may be initiated in several points in the network and propagate at multiple scales, with e.g., the cortex biasing hippocampal activity (8,57). This may enable neural plasticity continuous update of hippocampal representations as well as cortical representations, which may serve the function of keeping representations coherent across structures in face of substantial drift that has been observed across days (61,62). ...
Hippocampus–neocortex interactions during sleep are critical for memory processes: Hippocampally initiated replay contributes to memory consolidation in the neocortex and hippocampal sharp wave/ripples modulate cortical activity. Yet, the spatial and temporal patterns of this interaction are unknown. With voltage imaging, electrocorticography, and laminarly resolved hippocampal potentials, we characterized cortico-hippocampal signaling during anesthesia and nonrapid eye movement sleep. We observed neocortical activation transients, with statistics suggesting a quasi-critical regime, may be helpful for communication across remote brain areas. From activity transients, we identified, in a data-driven fashion, three functional networks. A network overlapping with the default mode network and centered on retrosplenial cortex was the most associated with hippocampal activity. Hippocampal slow gamma rhythms were strongly associated to neocortical transients, even more than ripples. In fact, neocortical activity predicted hippocampal slow gamma and followed ripples, suggesting that consolidation processes rely on bidirectional signaling between hippocampus and neocortex.
