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More than spikes: Common oscillatory mechanisms for content specific neural representations during perception and memory

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... Briefly, acquired iEEG data were first low-pass-filtered at 30 Hz, epoched around cue presentation, and converted into independent components (20). Our decision to use low-pass-filtered time series data as input to the RSA was motivated by the fact that raw time series data preserve the rich information content of the original signal, including both the power and phase of low-frequency activity (21), and have recently been shown to perform well in decoding analyses (17). Next, trials were randomly distributed onto two data halves. ...
... The current study focused on hippocampal theta phases rather than on power and examined their relationship to large-scale neural representations of different mental contents. This framework is motivated by recent advances in identifying phase-dependent representations as a component of the human neural code (21,30). With this mechanism, a separation of otherwise interfering mental contents may be achieved, an idea that prompts discussion of the relationship between our findings and previous working memory studies. ...
... Whereas these previous studies obtained stimulus-specific neural representations from patterns of gamma power-e.g., at letter-selective cortical sites (6)-we detected cue-specific neural representations from large-scale electrophysiological time series data dominated by activity below 30 Hz. This analysis was motivated by recent advances in decoding stimuli from time series data (17) and the fact that time series data retain the rich information content of the original signal (21). Nevertheless, detailed analyses showed that neural cue representations could also be observed from gamma power patterns in our study and that their strength was correlated with the strength of neural cue representations based on the time domain data. ...
Article
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Humans are adept in simultaneously following multiple goals, but the neural mechanisms for maintaining specific goals and distinguishing them from other goals are incompletely understood. For short time scales, working memory studies suggest that multiple mental contents are maintained by theta-coupled reactivation, but evidence for similar mechanisms during complex behaviors such as goal-directed navigation is scarce. We examined intracranial electroencephalography recordings of epilepsy patients performing an object-location memory task in a virtual environment. We report that large-scale electrophysiological representations of objects that cue for specific goal locations are dynamically reactivated during goal-directed navigation. Reactivation of different cue representations occurred at stimulus-specific hippocampal theta phases. Locking to more distinct theta phases predicted better memory performance, identifying hippocampal theta phase coding as a mechanism for separating competing goals. Our findings suggest shared neural mechanisms between working memory and goal-directed navigation and provide new insights into the functions of the hippocampal theta rhythm.
... The brain activity we focus on here are neural oscillations. Oscillations are a ubiquitous property of the brain, and are known to be modulated by various aspects of vision and memory in humans (Fell and Axmacher, 2011;Hanslmayr et al., 2012;Helfrich and Knight, 2016;Jensen et al., 2014;Watrous et al., 2015a). Recent studies have begun to show how the ongoing phase of an oscillation can be used to decode specific stimuli (Lopour et al., 2013;Ng et al., 2013;Schyns et al., 2011;Turesson et al., 2012;Watrous et al., 2015b). ...
... The analysis was based on phase patterns from MEG source localised data, with our results showing that objects with more similar properties have more similar spatio-temporal phase patterns in the mass signals recorded through MEG. It is believed that the phase of lowfrequency activity is suited for decoding stimulus properties for MEG, EEG and ECOG Watrous et al., 2015a), supported by a number of studies showing that oscillatory phase carries more information about the stimulus than power (Lopour et al., 2013;Ng et al., 2013;Schyns et al., 2011). While not presented here, we also see a similar pattern with our data. ...
... While not presented here, we also see a similar pattern with our data. While neural mass activity can be difficult to relate to the underlying neural activity, there is some suggestion that low-frequency phase of mass signals might index the timing of the underlying neural activity and its firing Watrous et al., 2015a). As such, our effects based on spatiotemporal phase patterns may be driven by spatiotemporal activity patterns of the mass neural populations, and further suggests that cognitively relevant properties are coded in distributed neural activity patterns in space and time. ...
Preprint
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Object recognition requires dynamic transformations of low-level visual inputs to complex semantic representations. While this process depends on the ventral visual pathway (VVP), we lack an incremental account from low-level inputs to semantic representations, and the mechanistic details of these dynamics. Here we combine computational models of vision with semantics, and test the output of the incremental model against patterns of neural oscillations recorded with MEG in humans. Representational Similarity Analysis showed visual information was represented in alpha activity throughout the VVP, and semantic information was represented in theta activity. Furthermore, informational connectivity showed visual information travels through feedforward connections, while visual information is transformed into semantic representations through feedforward and feedback activity, centered on the anterior temporal lobe. Our research highlights that the complex transformations between visual and semantic information is driven by feedforward and recurrent dynamics resulting in object-specific semantics.
... Another oscillatory signal that is involved in spatial navigation is the hippocampal gamma oscillation , which increases in power during movement [25,26] and interacts with theta oscillations, such that gamma power varies across the theta cycle [25,27]. This phenomenon of cross-frequency coupling between gamma power and theta phase generalizes to other cognitive domains [28] and is thought to reflect phase coding [29] to, for example, bind multiple stimuli into sequences. ...
... Since the discovery of the scalp EEG by Hans Berger during the 1920s, it has been known that cognition is accompanied by brain oscillations across a range of frequencies. EEG oscillations are classically divided into frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), low gamma , and high gamma (N50 Hz). Although it is more common to record these signals from the scalp, in rare circumstances researchers are able to measure these oscillations directly with high spatiotemporal resolution from the human brain, when neurosurgical patients are implanted with intracranial depth, strip, or grid macroelectrodes for clinical purposes ( Figure I). ...
... Theta oscillations may also support mnemonic processes by inducing synaptic modifications of intrahippocampal associational pathways [148] and may allow the creation of temporally ordered memories [149]. Finally, theta oscillations may underlie phase coding by allowing single neurons to represent information by varying the timing of their spiking relative to theta oscillations during perception, navigation, and memory [29]. Increasing optogenetic stimulation (blue light) frequency of these neurons (from 6 to 9 Hz) leads to higher theta oscillation frequency in CA1 and is followed by increased velocity of the rodent. ...
Article
Recent evidence suggests that mesoscopic neural oscillations measured via intracranial electroencephalography exhibit spatial representations, which were previously only observed at the micro- and macroscopic level of brain organization. Specifically, theta (and gamma) oscillations correlate with movement, speed, distance, specific locations, and goal proximity to boundaries. In entorhinal cortex (EC), they exhibit hexadirectional modulation, which is putatively linked to grid cell activity. Understanding this mesoscopic neural code is crucial because information represented by oscillatory power and phase may complement the information content at other levels of brain organization. Mesoscopic neural oscillations help bridge the gap between single-neuron and macroscopic brain signals of spatial navigation and may provide a mechanistic basis for novel biomarkers and therapeutic targets to treat diseases causing spatial disorientation. ***** Freely available from ScienceDirect until 8 August 2019: https://authors.elsevier.com/c/1ZFL14sIRvBFUe *****
... At a cellular level, content-specific "engrams cells" were identified in the rodent hippocampus 14,15 and neocortex [16][17] ; at the system level, multivariate analysis methods such as pattern classification 18 and representational similarity analysis 19 have been used to identify the representation of specific events (see ref. 12 for a review). Intracranial EEG (iEEG) recordings in epilepsy patients offer a unique opportunity to directly track the electrophysiological organization underlying content-specific representations and inter-regional information transfer at a fast time-scale 20,21 . Indeed, previous iEEG studies have shown that remembering an episode requires the reinstatement of a dynamical oscillatory state, the 'neural fingerprint' of a specific experience 22 . ...
... In the future, it will be important to investigate the directionality of these interactions and relate them to the representation of specific content at the single cell level (ref. 26 , see ref. 20 for a review). Given the differences in the timings of reinstatement we observe relative to previous literature, and the fact that action and active learning promote hippocampal-neocortical interactions 41 , we also highlight the need to further explore the specific role of active learning in modulating hippocampal and neocortical reinstatement. ...
... Interestingly, while LTC reinstatement depended on information carried across a wide range of frequencies, hippocampal reinstatement was mostly driven by low-frequency oscillations. This contrasts with previous iEEG studies in which either broadband 22,42 or high-frequency oscillations 23,25 were reported to encode stimulus-specific information in the hippocampus (for a review, see ref. 20 ). Given the active navigation task we deployed, one may hypothesize that the band-specific effects observed here are related to the tight coupling of hippocampal delta and theta oscillations with spatial navigation in humans (e.g., refs. ...
Article
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Theoretical models of episodic memory have proposed that retrieval depends on interactions between the hippocampus and neocortex, where hippocampal reinstatement of item-context associations drives neocortical reinstatement of item information. Here, we simultaneously recorded intracranial EEG from hippocampus and lateral temporal cortex (LTC) of epilepsy patients who performed a virtual reality spatial navigation task. We extracted stimulus-specific representations of both item and item-context associations from the time-frequency patterns of activity in hippocampus and LTC. Our results revealed a double dissociation of representational reinstatement across time and space: an early reinstatement of item-context associations in hippocampus preceded a later reinstatement of item information in LTC. Importantly, reinstatement levels in hippocampus and LTC were correlated across trials, and the quality of LTC reinstatement was predicted by the magnitude of phase synchronization between hippocampus and LTC. These findings confirm that episodic memory retrieval in humans relies on coordinated representational interactions within a hippocampal-neocortical network.
... Neural oscillations have been hypothesized to disambiguate and sustain sequence representations. [9][10][11][12] Specifically, an influential theoretical framework [13][14][15][16][17] focused on theta-nested gamma oscillations [18][19][20][21] has generated two predictions about the oscillatory mechanisms underlying sequence memory. First, interference between individual memory items is minimized by temporally segregating stimulus-specific gamma activity 12,[21][22][23][24][25] within distinct phases of a theta cycle or across separate theta cycles. ...
... [9][10][11][12] Specifically, an influential theoretical framework [13][14][15][16][17] focused on theta-nested gamma oscillations [18][19][20][21] has generated two predictions about the oscillatory mechanisms underlying sequence memory. First, interference between individual memory items is minimized by temporally segregating stimulus-specific gamma activity 12,[21][22][23][24][25] within distinct phases of a theta cycle or across separate theta cycles. Second, items are retained via the sequential reactivation of each memory trace, with replay of the entire sequence temporally compressed into theta timescales to allow multiple items to be maintained in working memory. ...
... From a theoretical perspective, such diversity is thought to be critical for a physiological substrate capable of representing multiple streams of information. 9,12 To further demonstrate the task-dependent emergence of these nested oscillations, we conducted two additional analyses. First, using data from the power spectra of each task period (see single-subject, e.g., in Figures S3A and S3B), 44 we confirmed that there was a clear theta-band peak in both regions of interest ( Figure S3C). ...
Article
Encoding and retaining novel sequences of sensory stimuli in working memory is crucial for adaptive behavior. A fundamental challenge for the central nervous system is to maintain each sequence item in an active and discriminable state, while also preserving their temporal context. Nested neural oscillations have been postulated to disambiguate the "what" and "when" of sequences, but the mechanisms by which these multiple streams of information are coordinated in the human brain remain unclear. Drawing from foundational animal studies, we recorded local field potentials from the human piriform cortex and hippocampus during a working memory task in which subjects experienced sequences of three distinct odors. Our data revealed a unique organization of odor memories across multiple timescales of the theta rhythm. During encoding, odors elicited greater gamma at distinct theta phases in both regions, time stamping their positions in the sequence, whereby the robustness of this effect was predictive of temporal order memory. During maintenance, stimulus-driven patterns of theta-coupled gamma were spontaneously reinstated in piriform cortex, recapitulating the order of the initial sequence. Replay events were time compressed across contiguous theta cycles, coinciding with periods of enhanced piriform-hippocampal theta-phase synchrony, and their prevalence forecasted subsequent recall accuracy on a trial-by-trial basis. Our data provide a novel link between endogenous replay orchestrated by the theta rhythm and short-term retention of sequential memories in the human brain.
... level of communication between neuronal populations (for example, contained in electroencephalography (EEG) oscillations; Watrous et al., 2015a) and on the larger-scale network level [among others captured by functional MRI (fMRI)]. It seems also possible that different levels of brain organization complement each other in constituting an engram. ...
... Second, Zhang et al. (2015) identified engrams of positions within virtual rooms via distributed patterns of EEG power in the gamma frequency range (42e120 Hz). Third, Watrous et al. (2015a) summarized evidence that both multiplexed oscillatory power and phase contribute to engrams at the mesoscopic scale, complementary to neuronal firing. Besides, EEG and MEG studies aim at identifying engrams similarly to iEEG studies. ...
... Power is the squared amplitude of EEG activity at a given frequency. Particularly theta and gamma power seem to be relevant for neural memory representations (Watrous et al., 2015a). • Phase (e.g., Watrous et al., 2015b). ...
Chapter
The concept of stimulus-specific memory traces, or engrams, suggests that the fate of individual memories can be tracked via empirically observable brain states. Recent advances in neuroscience have made it possible to discern these brain states in various ways. In animals, transgenics and optogenetics have allowed identifying distinct cell populations as engrams, which are necessary and sufficient for specific memory contents. In humans, individual memories have been associated with unique brain patterns via multivariate analysis techniques of electrophysiological and neuroimaging data. In the following chapter, we will review the advent of engram research in both animal and human memory studies. Specifically, we will provide methodological insights into multivariate pattern analysis and representational similarity analysis used to detect stimulus-specific memory traces in humans. We will conclude that the study of engrams goes beyond the understanding of general mechanisms of memory formation and retrieval, instead mastering the neurobiological mystery of individual memories.
... While spectral characteristic of neural activity have been previously investigated (Engel and Fries, 2010;Fries, 2015;Roux and Uhlhaas, 2014;Watrous et al., 2015;Weiss and Mueller, 2012) the neural mechanisms of long-range coupling of visual and other cortical regions following blindness are not yet understood (Gudi-Mindermann et al., 2018;Hawellek et al., 2013). The spectral characteristics of functional networks can be investigated by analyzing changes in synchronization of oscillatory brain activity (e.g., using electroencephalography, EEG or magnetoencephalography, MEG), which has been proposed to provide a mechanism for the formation of functional networks (Engel et al., , 1992Jutras and Buffalo, 2010;Watrous et al., 2015). ...
... While spectral characteristic of neural activity have been previously investigated (Engel and Fries, 2010;Fries, 2015;Roux and Uhlhaas, 2014;Watrous et al., 2015;Weiss and Mueller, 2012) the neural mechanisms of long-range coupling of visual and other cortical regions following blindness are not yet understood (Gudi-Mindermann et al., 2018;Hawellek et al., 2013). The spectral characteristics of functional networks can be investigated by analyzing changes in synchronization of oscillatory brain activity (e.g., using electroencephalography, EEG or magnetoencephalography, MEG), which has been proposed to provide a mechanism for the formation of functional networks (Engel et al., , 1992Jutras and Buffalo, 2010;Watrous et al., 2015). In sighted individuals, neuronal oscillatory activity in the theta- (Roux and Uhlhaas, 2014), beta- (Engel and Fries, 2010;Kopell et al., 2011;Spitzer and Haegens, 2017;Weiss and Mueller, 2012), and gamma-band (Pesaran et al., 2002;Roux et al., 2012) has been associated with working memory maintenance, and theta-and beta-band neural networks have been shown to be modulated by working memory training (Astle et al., 2015;Langer et al., 2013). ...
Article
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Congenitally blind individuals have been shown to activate the visual cortex during non-visual tasks. The neuronal mechanisms of such cross-modal activation are not fully understood. Here, we used an auditory working memory training paradigm in congenitally blind and in sighted adults. We hypothesized that the visual cortex gets integrated into auditory working memory networks, after these networks have been challenged by training. The spectral profile of functional networks was investigated which mediate cross-modal reorganization following visual deprivation. A training induced integration of visual cortex into task-related networks in congenitally blind individuals was expected to result in changes in long-range functional connectivity in the theta-, beta- and gamma band (imaginary coherency) between visual cortex and working memory networks. Magnetoencephalographic data were recorded in congenitally blind and sighted individuals during resting state as well as during a voice-based working memory task; the task was performed before and after working memory training with either auditory or tactile stimuli, or a control condition. Auditory working memory training strengthened theta-band (2.5–5 Hz) connectivity in the sighted and beta-band (17.5–22.5 Hz) connectivity in the blind. In sighted participants, theta-band connectivity increased between brain areas typically involved in auditory working memory (inferior frontal, superior temporal, insular cortex). In blind participants, beta-band networks largely emerged during the training, and connectivity increased between brain areas involved in auditory working memory and as predicted, the visual cortex. Our findings highlight long-range connectivity as a key mechanism of functional reorganization following congenital blindness, and provide new insights into the spectral characteristics of functional network connectivity.
... The dynamics of working memory networks can be investigated by analyzing changes in synchronization of oscillatory brain activity (e.g. using electroencephalography, EEG or magnetoencephalography, MEG), which has been proposed to provide a mechanism for the formation of functional networks (Engel et al. 1992Jutras and Buffalo 2010;Watrous et al. 2015). Importantly, this approach extends previous fMRI research, as it allows to characterize the spectral characteristics of functional networks, providing additional insights into neural coupling mechanisms involved in crossmodal reorganization (Engel and Fries 2010;Weiss and Mueller 2012;Roux and Uhlhaas 2014;Watrous et al. 2015). ...
... The dynamics of working memory networks can be investigated by analyzing changes in synchronization of oscillatory brain activity (e.g. using electroencephalography, EEG or magnetoencephalography, MEG), which has been proposed to provide a mechanism for the formation of functional networks (Engel et al. 1992Jutras and Buffalo 2010;Watrous et al. 2015). Importantly, this approach extends previous fMRI research, as it allows to characterize the spectral characteristics of functional networks, providing additional insights into neural coupling mechanisms involved in crossmodal reorganization (Engel and Fries 2010;Weiss and Mueller 2012;Roux and Uhlhaas 2014;Watrous et al. 2015). ...
Preprint
Congenitally blind individuals activate the visual cortex during non-visual tasks. Such crossmodal reorganization is likely associated with changes in large-scale functional connectivity, the spectral characteristics of which can be assessed by analysis of neural oscillations. To test visual cortical integration into working memory networks, we recorded magnetoencephalographic data from congenitally blind and sighted individuals during resting state as well as during a voice-based working memory task prior to and following working memory training with voices, or tactile stimuli or a training-control condition. Auditory training strengthened beta-band (17.5-22.5 Hz) connectivity (imaginary coherency) in the blind and theta-band (2.5-5 Hz) connectivity in the sighted during the task, suggesting different neural coupling mechanisms. In the sighted, theta-band connectivity increased between brain areas involved in auditory working memory (inferior frontal, superior temporal, insular cortex). In the blind, beta-band networks largely emerged during the training, and connectivity increased between brain areas involved in auditory working memory and the visual cortex. The prominent involvement of the right fusiform face area in this beta-band network suggests a task-specific integration of visual cortex. Our findings highlight large-scale interactions as a key mechanism of functional reorganization following congenital blindness, and provide new insights into the spectral characteristics of the mechanism.
... Here we take neural codes, in the strong sense of Watrous et al. (2015), to mean "that neural computation is causally driven by some configuration of spikes or extracellular signal, which implies that the brain is using this code to represent information." Defined this way, neural codes are the functional "signals of the system." ...
... Different types of information can also be multiplexed using multiple oscillatory frequencies of ensembles and populations to encode specific types of information. An example is "the spectral fingerprint hypothesis, which posits that different frequencies of oscillations underlie different cognitive operations" (Watrous et al., 2015). ...
Article
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Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Toward reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in candidate neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.
... Our results reveal that this HM theta-power emerges across time and is sensitive to environmental boundaries. From a systems perspective, our results demonstrate that neural representations occur at the level of mesoscopic oscillatory networks not only in rodents [27,28], but also in humans [29], and suggest shared properties between single-cell responses and network activity patterns. ...
Article
Grid cells and theta oscillations are fundamental components of the brain’s navigation system. Grid cells provide animals and humans with a spatial map of the environment by exhibiting multiple firing fields arranged in a regular grid of equilateral triangles. This unique firing pattern presumably con- stitutes the neural basis for path integration and may also enable navigation in visual and conceptual spaces . Theta frequency oscillations are a prominent mesoscopic network phenomenon during navigation in both rodents and humans and encode movement speed , distance traveled , and proximity to spatial boundaries . Whether theta oscillations may also carry a grid-like signal remains elusive, however. Capitalizing on pre- vious fMRI studies revealing a macroscopic proxy of sum grid cell activity in human entorhinal cortex (EC) , we examined intracranial EEG recordings from the EC of epilepsy patients (n = 9) performing a virtual navigation task. We found that the power of theta oscillations (4–8 Hz) exhibits 6-fold rotational modulation by movement direction, reminiscent of grid cell-like representations detected using fMRI. Modulation of theta power was specific to 6-fold rotational symmetry and to the EC. Hexadirectional modulation of theta power by movement direction only emerged during fast movements, stabilized over the course of the experiment, and showed sensitivity to the environmental boundary. Our re- sults suggest that oscillatory power in the theta frequency range carries an imprint of sum grid cell activity potentially enabled by a common grid orien- tation of neighboring grid cells .
... [33]. Assuming that the frequency and phase of oscillations represent information and cognitive operations at a high level of specificity [34][35][36][37][38], one should be able to identify the reactivation of such contentspecific oscillatory patterns during memory retrieval. Because the investigation of similarities in oscillatory activity between encoding and retrieval offers ways to study the neural underpinnings of memory reactivation on multiple levels with several methods, oscillations became a major target in searching for the neural mechanisms underlying memory reactivation. ...
Article
The reactivation of neural activity that was present during the encoding of an event is assumed to be essential for human episodic memory retrieval and the consolidation of memories during sleep. Pioneering animal work has already established a crucial role of memory reactivation to prepare and guide behaviour. Research in humans is now delineating the neural processes involved in memory reactivation during both wakefulness and sleep as well as their functional significance. Focusing on the electrophysiological signatures of memory reactivation in humans during both memory retrieval and sleep-related consolidation, this review provides an overview of the state of the art in the field. We outline recent advances, methodological developments and open questions and specifically highlight commonalities and differences in the neuronal signatures of memory reactivation during the states of wakefulness and sleep. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.
... The present findings are consistent with frameworks that propose specific roles of different frequency bands, i.e., spectral fingerprints of cognitive processing [26,27] of different frequency bands to multiplex content-specific memory processes [51]. The dissociable roles of gamma and alpha/beta band oscillations have been studied in attention tasks [12,52]. ...
Article
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Decreases in low-frequency power (2-30 Hz) alongside high-frequency power increases (>40 Hz) have been demonstrated to predict successful memory formation. Parsimoniously, this change in the frequency spectrum can be explained by one factor, a change in the tilt of the power spectrum (from steep to flat) indicating engaged brain regions. A competing view is that the change in the power spectrum contains several distinct brain oscillatory fingerprints, each serving different computations. Here, we contrast these two theories in a parallel magnetoencephalography (MEG)-intracranial electroencephalography (iEEG) study in which healthy participants and epilepsy patients, respectively, studied either familiar verbal material or unfamiliar faces. We investigated whether modulations in specific frequency bands can be dissociated in time and space and by experimental manipulation. Both MEG and iEEG data show that decreases in alpha/beta power specifically predicted the encoding of words but not faces, whereas increases in gamma power and decreases in theta power predicted memory formation irrespective of material. Critically, these different oscillatory signatures of memory encoding were evident in different brain regions. Moreover, high-frequency gamma power increases occurred significantly earlier compared to low-frequency theta power decreases. These results show that simple "spectral tilt" cannot explain common oscillatory changes and demonstrate that brain oscillations in different frequency bands serve different functions for memory encoding.
... Interestingly, spikes locked to various oscillatory phases in the theta-frequency range, whereas they preferentially locked to the oscillatory troughs in the gamma-frequency range [16]. This observation is in line with theoretical models on theta-phase coding [71,72], which suggest that information about cognitive stimuli or cognitive states is not only encoded in the firing rate of the neurons but also in the phase of the theta oscillations at which the neurons' action potentials occur [13,73,74]. Furthermore, spike-phase locking in the human hippocampus and amygdala has been described during successful memory formation [18] and when human subjects hold multiple working memories in mind [74]. ...
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Local field potentials (LFPs) can provide biomarkers of cognitive function at the meso-scale level of brain organization. LFPs represent the aggregation of thousands of transmembrane currents from local and non-local brain regions and it is thus still not understood how LFPfluctuations relate to action potentials of single neurons. Because these action potentials represent key units of computation in the human brain it is important to understand how they relate to and interact with LFPs during human cognitive functioning. Using intracranial probes which include both macro- and micro- electrodes, researchers can simultaneously measure synchronous changes in single-neuron action potentials and LFPs with respect to human cognition and behavior. In this chapter, we describe recent advances in recording and analyzing simultaneous single-neuron spiking and LFP oscillations. We provide a practical guide to estimating the relationship between neural spiking, LFP power, and LFP phase—with a specific focus on recent approaches to investigating how different LFP oscillations might modulate the timing of single-neuron spiking.We describe how the relationship between the LFP and single-neuron spiking is thought to be functionallyimportant for representing information in the brain, and suggest that studying this relationship has broad relevance for understanding the neurophysiological mechanisms underlying human cognition.
... However, so far no study has shown that entrainment occurs at different phases for different neuronal populations dependent on temporal occurrence of the task-relevant input. Alternatively, there is not one optimal, most sensitive phase, but phase is used as a cue for specific content (Jensen et al., 2014;Watrous et al., 2015;Bahramisharif et al., 2018) as would be predicted by models suggesting that high frequency oscillations nested in low frequencies represent information content (Jensen et al., 2012;Lisman and Jensen, 2013). Indeed, we have previously suggested that temporal information might be encoded by the phase of ongoing oscillations (Ten Oever and Sack, 2015). ...
Article
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The brain is inherently proactive, constantly predicting the when (moment) and what (content) of future input in order to optimize information processing. Previous research on such predictions has mainly studied the “when” or “what” domain separately, missing to investigate the potential integration of both types of predictive information. In the absence of such integration, temporal cues are assumed to enhance any upcoming content at the predicted moment in time (general temporal predictor). However, if the when and what prediction domain were integrated, a much more flexible neural mechanism may be proposed in which temporal-feature interactions would allow for the creation of multiple concurrent time-content predictions (parallel time-content predictor). Here, we used a temporal association paradigm in two experiments in which sound identity was systematically paired with a specific time delay after the offset of a rhythmic visual input stream. In Experiment 1, we revealed that participants associated the time delay of presentation with the identity of the sound. In Experiment 2, we unexpectedly found that the strength of this temporal association was negatively related to the EEG steady-state evoked responses (SSVEP) in preceding trials, showing that after high neuronal responses participants responded inconsistent with the time-content associations, similar to adaptation mechanisms. In this experiment, time-content associations were only present for low SSVEP responses in previous trials. These results tentatively show that it is possible to represent multiple time-content paired predictions in parallel, however, future research is needed to investigate this interaction further.
... The brain activities we focus on here are neural oscillations. Oscillations are a ubiquitous property of the brain and are known to be modulated by various aspects of vision and memory in humans (Helfrich & Knight, 2016;Watrous, Fell, Ekstrom, & Axmacher, 2015;Jensen et al., 2014;Hanslmayr, Staudigl, & Fellner, 2012;Fell & Axmacher, 2011). Recent studies have begun to show how the ongoing phase of an oscillation can be used to decode specific stimuli (Michelmann, Bowman, & Hanslmayr, 2016;Staudigl, Vollmar, Noachtar, & Hanslmayr, 2015;Watrous, Deuker, Fell, & Axmacher, 2015;Lopour, Tavassoli, Fried, & Ringach, 2013;Ng, Logothetis, & Kayser, 2013;Kayser, Ince, & Panzeri, 2012;Turesson, Logothetis, & Hoffman, 2012;Schyns, Thut, & Gross, 2011;Montemurro, Rasch, Murayama, Logothetis, & Panzeri, 2008). ...
Article
Object recognition requires dynamic transformations of low-level visual inputs to complex semantic representations. Although this process depends on the ventral visual pathway, we lack an incremental account from low-level inputs to semantic representations and the mechanistic details of these dynamics. Here we combine computational models of vision with semantics and test the output of the incremental model against patterns of neural oscillations recorded with magnetoencephalography in humans. Representational similarity analysis showed visual information was represented in low-frequency activity throughout the ventral visual pathway, and semantic information was represented in theta activity. Furthermore, directed connectivity showed visual information travels through feedforward connections, whereas visual information is transformed into semantic representations through feedforward and feedback activity, centered on the anterior temporal lobe. Our research highlights that the complex transformations between visual and semantic information is driven by feedforward and recurrent dynamics resulting in object-specific semantics.
... One possibility is that large-scale corticocortical communication is modulated by neural oscillations. Oscillations in neural activity are readily observed throughout the human brain at various spatiotemporal scales [1][2][3][4] and have been linked with a variety of cognitive functions [5][6][7][8][9][10][11]. An emerging literature has suggested that synchronized oscillations could serve as a gating mechanism to quickly enable the selective routing of information between brain regions [12][13][14][15][16]. ...
Article
Recent evidence has suggested that coherent neuronal oscillations may serve as a gating mechanism for flexibly modulating communication between brain regions. For this to occur, such oscillations should be robust and coherent between brain regions that also demonstrate time-locked correlations, with time delays that match the phase delays of the coherent oscillations. Here, by analyzing functional connectivity in both the time and frequency domains, we demonstrate that alpha oscillations satisfy these constraints and are well suited for modulating communication over large spatial scales in the human brain. We examine intracranial EEG in the human temporal lobe and find robust alpha oscillations that are coherent between brain regions with center frequencies that are consistent within each individual participant. Regions demonstrating coherent narrowband oscillations also exhibit time-locked broadband correlations with a consistent time delay, a requirement for an efficient communication channel. The phase delays of the coherent alpha oscillations match the time delays of the correlated components, and importantly, both broadband correlations and neuronal spiking activity are modulated by the phase of the oscillations. These results are specific to the alpha band and build upon emerging evidence suggesting that alpha oscillations may play an active role in cortical function. Our data therefore provide evidence that large-scale communication in the human brain may be rhythmically modulated by alpha oscillations.
... Consider the abstract example of multiplexing given by Watrous et al. ([2015]) in Figure 1. Suppose that a single cell or group of cells exhibits the responses to 'light' and 'dark' stimuli shown in the bottom part of (a). ...
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Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. 'Holists' posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of decomposition, which I call 'atomism', and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, 'contextualist' notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty-one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism.
... Having said this, there are neurobiological memory models, such as the spectro-contextual encoding and retrieval theory (SCERT), which argue that oscillatory activity in any frequency band, rather than specifically gamma band activity, can underlie the selective enhancement of memories (Canavier, 2015;Hanslmayr & Staudigl, 2014;Siegel, Donner, & Engel, 2012;Sutterer, Foster, Serences, Vogel, & Awh, 2018;Watrous & Ekstrom, 2014;Watrous, Fell, Ekstrom, & Axmacher, 2015;Watrous, Miller, Qasim, Fried, & Jacobs, 2018). Specifically, SCERT emphasises that oscillatory activity occurs at different frequencies between different encoding events, and it is the reinstatement of this frequency-specific oscillatory activity during retrieval which underlies neural reinstatement (hereinafter referred to as oscillatory reinstatement) and thus selective memory enhancement. ...
Article
How is the strength of a memory determined? This review discusses three main factors that contribute to memory enhancement - 1) emotion, 2) targeted memory reactivation, and 3) neural reinstatement. Whilst the mechanisms through which memories become enhanced vary, this review demonstrates that activation of the basolateral amygdala and hippocampal formation are crucial for facilitating encoding, consolidation, and retrieval. Here we suggest methodological factors to consider in future studies, and discuss several unanswered questions that should be pursued in order to clarify selective memory enhancement.
... Finally, it is important to note that theta activity is unlikely to be the sole oscillatory brain mechanism regulating semantic cognition. Electrophysiological studies have shown that semantic processing is associated with changes across multiple oscillatory frequencies and cortical regions, suggesting that different frequency bands and their interactions might reflect (and possibly implement) distinct aspects of the retrieval function, such as memory reinstatement 50 , semantic conflict 51 , or sentence processing 22 . In particular, alpha-band (10 Hz) oscillatory activity has been associated with inhibitory functions and controlled access to stored knowledge representations 52 . ...
Article
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Lexical–semantic retrieval emerges through the interactions of distributed prefrontal and perisylvian brain networks. Growing evidence suggests that synchronous theta band neural oscillations might play a role in this process, yet, their functional significance remains elusive. Here, we used transcranial alternating current stimulation to induce exogenous theta oscillations at 6 Hz (θ-tACS) over left prefrontal and posterior perisylvian cortex with a 180° (anti-phase) and 0° (in-phase) relative phase difference while participants performed automatic and controlled retrieval tasks. We demonstrate that θ-tACS significantly modulated the retrieval performance and its effects were both task- and phase-specific: the in-phase tACS impaired controlled retrieval, whereas the anti-phase tACS improved controlled but impaired automatic retrieval. These findings indicate that theta band oscillatory brain activity supports binding of semantically related representations via a phase-dependent modulation of semantic activation or maintenance.
... Unlike the single-cell results in rodents, however, stimulus-specific representations were analyzed on a network level. The relationship between stimulus-specific representations at the level of single units and in large-scale brain networks remains to be addressed in future studies in both animals and humans 34 . ...
Article
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Consolidation stabilizes memory traces after initial encoding. Rodent studies suggest that memory consolidation depends on replay of stimulus-specific activity patterns during fast hippocampal "ripple" oscillations. Here, we measured replay in intracranial electroencephalography recordings in human epilepsy patients, and related replay to ripples. Stimulus-specific activity was identified using representational similarity analysis and then tracked during waking rest and sleep after encoding. Stimulus-specific gamma (30-90 Hz) activity during early (100-500 ms) and late (500-1200 ms) encoding is spontaneously reactivated during waking state and sleep, independent of later memory. Ripples during nREM sleep, but not during waking state, trigger replay of activity from the late time window specifically for remembered items. Ripple-triggered replay of activity from the early time window during nREM sleep is enhanced for forgotten items. These results provide the first electrophysiological evidence for replay related to memory consolidation in humans, and point to a prominent role of nREM ripple-triggered replay in consolidation processes.
... This interpretation applies to our finding that aperiodic and beta activity showed a negative association with performance for fixed and flexible word orders: An increase in beta power predicted more sensitive behavioral responses for fixed sentences, whereas reduced beta predicted performance for flexible word orders. These findings can be explained by integrating two perspectives: the "spectral fingerprints" hypothesis (Keitel & Gross, 2016;Watrous, Fell, Ekstrom, & Axmacher, 2015;Hanslmayr & Staudigl, 2014;Womelsdorf, Valiante, Sahin, Miller, & Tiesinga, 2014;Siegel, Donner, & Engel, 2012) and generalized predictive coding (Friston, 2010(Friston, , 2018(Friston, , 2019. ...
Article
Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.
... This relationship becomes more complex when examining the interactive influence of beta and 1/ƒ activity on behaviour: when the 1/ƒ slope is shallow, the effect of beta activity on fixed and flexible word order processing is strongest; however, when the 1/ƒ is steep, the effect of beta activity on accurate sentence processing for both fixed and flexible word orders diminishes. We believe that this pattern of results can be explained by integrating two perspectives; namely the "spectral fingerprints" hypothesis (Hanslmayr & Staudigl, 2014;Keitel & Gross, 2016;Siegel et al., 2012;Watrous et al., 2015;Womelsdorf et al., 2014) and models of hierarchical predictive coding (Friston, 2010(Friston, , 2019(Friston, , 2020. ...
Preprint
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Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Further, recent work has shown that non-oscillatory (1/ f ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ f and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Fixed word order rules were associated with a steeper 1/ f slope, while flexible word order rules were associated with a shallower slope. We also show that increased alpha/beta power predicts fixed relative to flexible word order rule learning and behavioural performance. Together, these results suggest that 1/ f activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word order permutations, which manifest in distinct oscillatory profiles. SIGNIFICANCE STATEMENT Characterising the neurobiological basis of cognition requires a deep understanding of how neural populations communicate. Here we show that 1/ f and oscillatory activity interact during a complex language learning task to influence sentence processing. In addition, we observe that this interaction manifests on multiple spatiotemporal scales, with 1/ f activity becoming less topographically distributed over the time course of learning. These results significantly add to our understanding of how aperiodic and oscillatory activity interact during higher-order cognitive processing, providing novel insights into how neural communication supports the learning and retrieval of complex language-related rules.
... grouping together) information into one simple combined signal. This mechanism has been implicated in many cognitive functions such as memory (for a review see Canolty & Knight, 2010;Watrous, Fell, Ekstrom, & Axmacher, 2015): where, as mentioned earlier, theta oscillations are thought to provide a reference mechanism for gamma oscillation in the hippocampus (Lisman & Jensen, 2013). Another example of the presence of cross-frequency coupling comes from the coding of facial expressions. ...
Thesis
Our brain activity is inherently rhythmic: oscillations can be found at all levels of organization. This rhythmicity in brain activity gives a rhythm to what we see: instead of continuously monitoring the environment, our brains take "snapshots" of the external world from 5 to 15 times a second. This creates perceptual cycles: depending on the phase of the underlying oscillation, our perceptual abilities fluctuate. Accumulating evidence shows that brains oscillations at various frequencies are instrumental in shaping visual perception. At the heart of this thesis lies the White Noise Paradigm, which we designed as a tool to better understand the influence of oscillations on visual perception and which ultimately could be used to control visual perception. The White Noise Paradigm uses streams of flashes with random luminance (i.e. white noise) as stimuli, which have been shown to constrain brain oscillations in a predictable manner. The impulse response to WN sequences has a strong (subject specific) oscillatory component at ~10Hz akin to a perceptual echo. Since the impulse response is a model of how our brains respond to one single flash in the sequence, they can be used to reconstruct (rather than record) the brain activity to new stimulation sequences. We then present near-perceptual threshold targets embedded within the WN sequences and extract the time course of these predicted/reconstructed background oscillations around target presentation. Thus, the reconstructed EEG can be used to study the influence of the oscillatory components on visual perception, independently of other types of signals usually recorded in the EEG. First, we validate the White Noise Paradigm by showing that: 1) the WN sequences do modulate behaviour, 2) the perceptual echoes evoked by these WN sequences are stable in time, 3) they are a (relatively) good model of the subject's recorded brain activity and 4) their neuronal basis can be found in the early visual areas. Second, we investigate the relationship between these constrained brain oscillations and visual perception. Specifically, we show that the reconstructed EEG can help us recover the true latency at which (theta) phase influences perception. Moreover, it can help us uncover a causal influence of (alpha) power on target detection, independently from any fluctuation in endogenous factors. Finally, capitalizing on the link between oscillations and perception, we build two algorithms used to control the perception of subjects. First, we build a "universal" forward model which can predict for any observer whether a particular target will be seen or not. Second, we build a subject-dependent model which can predict whether a particular subject (for whom EEG was recorded previously) will perceive a given target or not. Critically, this can be used to present targets optimized to be perceived by one subject only, to the detriment of all other subjects, creating a sort of "Neuro-Encryption" system.
... One way of looking at population activity is by measuring local field potentials (LFPs)-oscillatory patterns at the population level that reflect a sum of overall electrical activity in the population (Burnston, forthcoming;Canolty and Knight 2010;Watrous et al. 2015). LFPs oscillate at particular frequencies and amplitudes, and changes in frequency and amplitude often correlate with changes in function. ...
Article
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The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea (2018). Still, I am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation.
... This relationship becomes more complex when examining the interactive influence of beta and 1/ƒ activity on behaviour: when the 1/ƒ slope is shallow, the effect of beta activity on fixed and flexible word order processing is strongest; however, when the 1/ƒ is steep, the effect of beta activity on accurate sentence processing for both fixed and flexible word orders diminishes. We believe that this pattern of results can be explained by integrating two perspectives; namely the "spectral fingerprints" hypothesis (Hanslmayr & Staudigl, 2014;Keitel & Gross, 2016;Siegel et al., 2012;Watrous et al., 2015;Womelsdorf et al., 2014) and models of hierarchical predictive coding (Friston, 2010(Friston, , 2019(Friston, , 2020. ...
Thesis
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The aim of this thesis was to improve our understanding of the neural basis of language learning, focussing on markers of sleep-associated memory consolidation. To this end, the studies reported here tested the hypothesis that the generalisation of sentence-level regularities benefits from sleep-based neurophysiological activity, including slow oscillations and sleep spindles. In addition to building on the sleep and language learning literature, this thesis aimed to: (1) develop a cross-linguistically informed miniature language paradigm to study higher-order language learning; (2) characterise the neural oscillatory mechanisms underlying the encoding, (sleep-based) consolidation and comprehension of sentence-level information; and (3) develop a neurobiologically plausible model of how sleep consolidates language-related rules and how these effects manifest in task-related neural activity. Chapter 1 summarises major theories of sleep-based memory consolidation and a neurobiological, cross-linguistic model of sentence comprehension. We propose a novel integration of these models based on notions of hierarchical predictive coding, a unified theory of brain function. Chapter 2 then synthesises studies on sleep and language learning and outlines testable hypotheses that focus on sleep-related effects on oscillatory activity during sentence processing within a newly learned language. Data are presented in Chapter 3 from a study verifying the utility of a novel modified miniature language paradigm modelled on Mandarin Chinese (Mini Pinyin). Results demonstrate that monolingual native English speakers can rapidly learn complex grammatical rules and that language learning is related to inter-individual differences in statistical learning ability, as well as similarities to the rules of comprehenders’ native language. In order to characterise the neural mechanisms underlying the encoding of Mini Pinyin, Chapter 4 describes an EEG experiment that quantified task-evoked spectral power and broadband aperiodic activity to predict grammar learning and acceptability judgements. Results demonstrate that aperiodic activity plays an important role in higher-order cognition, including language learning, and that grammar learning is modulated by fixed and flexible word orders, which manifest in distinct oscillatory profiles during incremental sentence processing. Finally, Chapter 5 presents data demonstrating an association between sleep neurophysiology, task-related oscillatory activity and behaviour: slow wave-spindle coupling during non-rapid eye movement sleep predicted the generalisation of sentence-level regularities. This effect also interacted with task-related oscillatory power, possibly reflecting learning-related alterations in the underlying neuronal populations engaged during sentence processing. Together, these results highlight the beneficial influence of sleep on language learning, providing evidence that adds to neurobiological models of sleep-associated memory consolidation and models of sentence processing. In Chapter 6, these models are re-evaluated in light of this evidence. Proposals are made on how theories of sleep-associated memory consolidation and sentence processing can be better integrated with the goal of developing a neurobiologically inspired model of cognition that views language as a complex form of memory that is organised across both wake and sleep.
... Contemporary theories of episodic memory argue for the integration and segregation of information distributed between the hippocampus and neocortex as central to memory organization [161,269]. Neural oscillations exert an influence on both local and distributed neural populations and may subserve integrative functions (reviewed in [21, 270,271]). It follows that interrogation of oscillations should be informative for understanding behaviors which require integration over scales. ...
Article
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Objective: Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach: We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results: We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance: This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.
... It is well known that memory retrieval depends both on individual neuronal firing behaviour and the synchronous oscillations of spatially distributed neurons [62]. A dynamic E/I balance is essential for maintaining neuronal firing behaviour and the oscillatory patterns of hippocampal neurons [63]. ...
Article
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Stressful life events induce abnormalities in emotional and cognitive behaviour. The endogenous opioid system plays an essential role in stress adaptation and coping strategies. In particular, the µ-opioid receptor (μR), one of the major opioid receptors, strongly influences memory processing in that alterations in μR signalling are associated with various neuropsychiatric disorders. However, it remains unclear whether μR signalling contributes to memory impairments induced by acute stress. Here, we utilized pharmacological methods and cell-type-selective/non-cell-type-selective μR depletion approaches combined with behavioural tests, biochemical analyses, and in vitro electrophysiological recordings to investigate the role of hippocampal μR signalling in memory-retrieval impairment induced by acute elevated platform (EP) stress in mice. Biochemical and molecular analyses revealed that hippocampal μRs were significantly activated during acute stress. Blockage of hippocampal μRs, non-selective deletion of μRs or selective deletion of μRs on GABAergic neurons (μRGABA) reversed EP-stress-induced impairment of memory retrieval, with no effect on the elevation of serum corticosterone after stress. Electrophysiological results demonstrated that stress depressed hippocampal GABAergic synaptic transmission to CA1 pyramidal neurons, thereby leading to excitation/inhibition (E/I) imbalance in a μRGABA-dependent manner. Pharmaceutically enhancing hippocampal GABAA receptor-mediated inhibitory currents in stressed mice restored their memory retrieval, whereas inhibiting those currents in the unstressed mice mimicked the stress-induced impairment of memory retrieval. Our findings reveal a novel pathway in which endogenous opioids recruited by acute stress predominantly activate μRGABA to depress GABAergic inhibitory effects on CA1 pyramidal neurons, which subsequently alters the E/I balance in the hippocampus and results in impairment of memory retrieval.
... The present findings are consistent with frameworks that propose specific roles of different frequency bands, i.e., spectral fingerprints of cognitive processing [26,27] of different frequency bands to multiplex content-specific memory processes [51]. The dissociable roles of gamma and alpha/beta band oscillations have been studied in attention tasks [12,52]. ...
Article
Decreases in low frequency power (2-30 Hz) alongside high frequency power increases (>40 Hz) have been demonstrated to predict successful memory formation. Parsimoniously this change in the frequency spectrum can be explained by one factor, a change in the tilt of the power spectrum (from steep to flat) indicating engaged brain regions. A competing view is that the change in the power spectrum contains several distinct brain oscillatory fingerprints, each serving different computations. Here, we contrast these two theories in a parallel MEG-intracranial EEG study where healthy participants and epilepsy patients, respectively, studied either familiar verbal material, or unfamiliar faces. We investigated whether modulations in specific frequency bands can be dissociated in time, space and by experimental manipulation. Both, MEG and iEEG data, show that decreases in alpha/beta power specifically predicted the encoding of words, but not faces, whereas increases in gamma power and decreases in theta power predicted memory formation irrespective of material. Critically, these different oscillatory signatures of memory encoding were evident in different brain regions. Moreover, high frequency gamma power increases occurred significantly earlier compared to low frequency theta power decreases. These results speak against a “spectral tilt” and demonstrate that brain oscillations in different frequency bands serve different functions for memory encoding.
... We make no use of precise spike times, but assume that some meta-circuitry organizes (temporally collects) transmission of en masse synaptic signals from an SDC coding field within some small window of arrival at a downstream decoding field. Our working assumption is that this is the purpose of the gamma cycle (Fries 2009, Buzsáki 2010, Igarashi, Lu et al. 2014, Watrous, Fell et al. 2015. ...
Preprint
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The brain is believed to implement probabilistic reasoning and to represent information via population, or distributed, coding. Most previous population-based probabilistic (PPC) theories share several basic properties: 1) continuous-valued neurons; 2) fully (densely)-distributed codes, i.e., all (most) units participate in every code; 3) graded synapses; 4) rate coding; 5) units have innate unimodal tuning functions (TFs); 6) intrinsically noisy units; and 7) noise/correlation is considered harmful. We present a radically different theory that assumes: 1) binary units; 2) only a small subset of units, i.e., a sparse distributed code (SDC) (cell assembly, ensemble), comprises any individual code; 3) binary synapses; 4) signaling formally requires only single (first) spikes; 5) units initially have completely flat TFs (all weights zero); 6) units are not inherently noisy; but rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, indirectly yielding correlation patterns. The theory, Sparsey, was introduced 20 years ago as a canonical cortical circuit/algorithm model, but not elaborated as an alternative to PPC theories. Here, we show that the active SDC simultaneously represents both the most similar/likely input and the coarsely-ranked distribution over all stored inputs (hypotheses). Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases. We also discuss our approach as a radically new implementation of graphical probability modeling.
... Neural oscillations are increasingly recognized as important mesoscopic components of the neural code (Buzsáki et al. 2012;Hanslmayr et al. 2012;Watrous et al. 2015a). Several lines of evidence across species and behaviors demonstrate that the frequency of neural oscillations varies across individuals and shifts to support neural communication and influence behavior (Cecere et al. 2015;Cohen 2014;Furman et al. 2018;Klimesch 1999;Mierau et al. 2017;Rudrauf et al. 2006;Watrous et al. 2013;Wutz et al. 2018). ...
Article
Neural oscillations are routinely analyzed using methods that measure activity in fixed frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm ("ORCA"), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands, which incorporates two new methods for frequency band identification. ORCA uses the instantaneous amplitude, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed "resting" conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that allows researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.
... We chose 180°because it has been a target in practical closed-loop experiments (Zrenner et al., 2018) and 300°because it is not easily detectable by peak-or zero-crossing detectors. Phase-locked closedloop experiments typically target lower frequency oscillations because they have implications in functional processes (Watrous et al., 2015) and are less affected by inherent system latencies. Using the MATLAB replication software, a phase-locked protocol is run targeting oscillations between 5Hz and 55Hz, with a step size of 1Hz, for the two phase targets within the selected IL data channel. ...
Preprint
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Background Closing the loop between brain activity and behavior is one of the most active areas of development in neuroscience. There is particular interest in developing closed-loop control of neural oscillations. Many studies report correlations between oscillations and functional processes. Oscillation-informed closed-loop experiments might determine whether these relationships are causal and would provide important mechanistic insights which may lead to new therapeutic tools. These closed-loop perturbations require accurate estimates of oscillatory phase and amplitude, which are challenging to compute in real time. New Method We developed an easy to implement, fast and accurate Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE). TORTE operates with the open-source Open Ephys GUI (OEGUI) system, making it immediately compatible with a wide range of acquisition systems and experimental preparations. Results TORTE efficiently extracts oscillatory phase and amplitude from a target signal and includes a variety of options to trigger closed-loop perturbations. Implementing these tools into existing experiments is easy and adds minimal latency to existing protocols. Comparison with Existing Methods Most labs use in-house lab-specific approaches, limiting replication and extension of their experiments by other groups. Accuracy of the extracted analytic signal and accuracy of oscillation-informed perturbations with TORTE match presented results by these groups. However, TORTE provides access to these tools in a flexible, easy to use toolkit without requiring proprietary software. Conclusion We hope that the availability of a high-quality, open-source, and broadly applicable toolkit will increase the number of labs able to perform oscillatory closed-loop experiments, and will improve the replicability of protocols and data across labs.
... Meanwhile, cross-frequency coupling models of VSTM suggest that individual items are represented by neural assemblies which are synchronized via high-frequency (i.e., gamma) oscillations that are locked to specific phases of hippocampal low-frequency oscillations (18)(19)(20). This coupling may result in phase coding, such that neural representations of specific items are coupled to distinct phases of low-frequency oscillations, according to either the identity of an item (21,22) or its position on a list (23). Notably, a very recent study integrated the concepts of activity-silent VSTM representations and phase coding by showing that the amplification of activity-silent working memory representations depends on the phase of ongoing electroencephalography (EEG) oscillations at which the impulse is applied (24). ...
Article
Visual short-term memory (VSTM) enables humans to form a stable and coherent representation of the external world. However, the nature and temporal dynamics of the neural representations in VSTM that support this stability are barely understood. Here we combined human intracranial electroencephalography (iEEG) recordings with analyses using deep neural networks and semantic models to probe the representational format and temporal dynamics of information in VSTM. We found clear evidence that VSTM maintenance occurred in two distinct representational formats which originated from different encoding periods. The first format derived from an early encoding period (250 to 770 ms) corresponded to higher-order visual representations. The second format originated from a late encoding period (1,000 to 1,980 ms) and contained abstract semantic representations. These representational formats were overall stable during maintenance, with no consistent transformation across time. Nevertheless, maintenance of both representational formats showed substantial arrhythmic fluctuations, i.e., waxing and waning in irregular intervals. The increases of the maintained representational formats were specific to the phases of hippocampal low-frequency activity. Our results demonstrate that human VSTM simultaneously maintains representations at different levels of processing, from higher-order visual information to abstract semantic representations, which are stably maintained via coupling to hippocampal low-frequency activity.
... It has been suggested that LFP oscillations may organize neurons into functional ensembles (Watrous et al., 2015;Helfrich and Knight, 2016). For example, coherence between PFC spikes and sensory cortex LFPs was increased during covert attention, and spike-LFP coherence within the sensory cortex was found to be correlated with behavioral performance. ...
Article
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Prefrontal cortex (PFC) are broadly linked to various aspects of behavior. During sensory discrimination, PFC neurons can encode a range of task related information, including the identity of sensory stimuli and related behavioral outcome. However, it remains largely unclear how different neuron subtypes and local field potential (LFP) oscillation features in the mouse PFC are modulated during sensory discrimination. To understand how excitatory and inhibitory PFC neurons are selectively engaged during sensory discrimination and how their activity relates to LFP oscillations, we used tetrode recordings to probe well-isolated individual neurons, and LFP oscillations, in mice performing a three-choice auditory discrimination task. We found that a majority of PFC neurons, 78% of the 711 recorded individual neurons, exhibited sensory discrimination related responses that are context and task dependent. Using spike waveforms, we classified these responsive neurons into putative excitatory neurons with broad waveforms or putative inhibitory neurons with narrow waveforms, and found that both neuron subtypes were transiently modulated, with individual neurons’ responses peaking throughout the entire duration of the trial. While the number of responsive excitatory neurons remain largely constant throughout the trial, an increasing fraction of inhibitory neurons were gradually recruited as the trial progressed. Further examination of the coherence between individual neurons and LFPs revealed that inhibitory neurons exhibit higher spike-field coherence with LFP oscillations than excitatory neurons during all aspects of the trial and across multiple frequency bands. Together, our results demonstrate that PFC excitatory neurons are continuously engaged during sensory discrimination, whereas PFC inhibitory neurons are increasingly recruited as the trial progresses and preferentially coordinated with LFP oscillations. These results demonstrate increasing involvement of inhibitory neurons in shaping the overall PFC dynamics toward the completion of the sensory discrimination task.
... When a bottom-up sensory input activates an ensemble, it temporarily oscillates in a gamma state (>30 Hz) and gives off a short burst of elevated spiking before inhibition reverts it back to the alpha and beta state and reduced spiking. The gamma bursts may be linked to underlying theta rhythms (Canolty et al., 2006;Voytek et al., 2015;Watrous et al., 2015). This could organize time-multiplexing of items (Bahramisharif et al., 2017;Fuentemilla et al., 2010;Herman et al., 2013). ...
Article
Working memory is the fundamental function by which we break free from reflexive input-output reactions to gain control over our own thoughts. It has two types of mechanisms: online maintenance of information and its volitional or executive control. Classic models proposed persistent spiking for maintenance but have not explicitly addressed executive control. We review recent theoretical and empirical studies that suggest updates and additions to the classic model. Synaptic weight changes between sparse bursts of spiking strengthen working memory maintenance. Executive control acts via interplay between network oscillations in gamma (30–100 Hz) in superficial cortical layers (layers 2 and 3) and alpha and beta (10–30 Hz) in deep cortical layers (layers 5 and 6). Deep-layer alpha and beta are associated with top-down information and inhibition. It regulates the flow of bottom-up sensory information associated with superficial layer gamma. We propose that interactions between different rhythms in distinct cortical layers underlie working memory maintenance and its volitional control. Miller et al. present a new model of working memory. Synaptic weight changes between sparse spiking help strengthen working memory maintenance. Interplay between alpha, beta, and gamma rhythms in different cortical layers provide an infrastructure for its volitional control.
... On the one hand, previous work using an IEM applied to alpha-band EEG activity, has successfully tracked covert spatial attention (Foster, Sutterer, et al. 2017), and spatial representations maintained in working memory (Foster et al. 2016;Foster, Bsales, et al. 2017). Recent theories about the role of rhythmic oscillations in memory maintain that same frequencies of oscillations coordinate specific cognitive operations at encoding and retrieval (Siegel et al. 2012;Watrous and Ekstrom 2014;Watrous et al. 2015), predicting that alpha-band activity may play a similar role at retrieval. In addition, alpha-band activity has been shown to track hemifield-specific location memory (Stokes et al. 2012;Waldhauser et al. 2016). ...
Preprint
A hallmark of episodic memory is the phenomenon of mentally re-experiencing the details of past events, and a well-established concept is that the neuronal activity that mediates encoding is reinstated at retrieval. Evidence for reinstatement has come from multiple modalities, including functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG). These EEG studies have shed light on the time-course of reinstatement, but have been limited to distinguishing between a few categories and/or limited measures of memory strength. The goal of this work was to investigate whether recently developed experimental and technical approaches, namely an inverted encoding model applied to alpha oscillatory power in conjunction with sensitive tests of memory retrieval in a continuous space, can track and reconstruct memory retrieval of specific spatial locations. In Experiment 1, we establish that an inverted encoding model applied to multivariate alpha topography can track retrieval of precise spatial memories. In Experiment 2, we demonstrate that the pattern of multivariate alpha activity at study is similar to the pattern observed during retrieval. Finally, we observe that these encoding models predict memory retrieval behavior, including the accuracy and latency of recall. These findings highlight the broad potential for using encoding models to characterize long-term memory retrieval.
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We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.
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Chapter
The alpha rhythm (AR) represents a fundamental oscillatory mechanism that controls information flow in the brain via rhythmic changes in neuronal excitability. AR is generated in the thalamus and neocortex and coordinated by the cortico-cortical and thalamo-cortical circuits. Human AR recorded by electroencephalogram (EEG) typically consists of a high-frequency occipito-parietal component (ARC1) and a low-frequency occipito-temporal component (ARC2). The selectivity of their responses to space versus object cognition tasks links ARC1 to the dorsal processing stream and ARC2 to the ventral stream. AR changes are the most pronounced EEG phenomena in the aging human brain. Both components slow down with aging and their sources shift in the anterior-inferior direction, but ARC1 evolves faster than ARC2. This causes a spatial and frequency overlap, leading to the transformation of the multicomponent AR into a single-component AR with age. Understanding these processes is essential for monitoring normal aging and neurodegenerative processes in the human brain.
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