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Rhythms of The Brain

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Abstract

Studies of mechanisms in the brain that allow complicated things to happen in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience. This book provides support for the idea that spontaneous neuron activity, far from being mere noise, is actually the source of our cognitive abilities. It looks at the co-evolution of structure and function in the mammalian brain, illustrating how self-emerged oscillatory timing is the brains fundamental organizer of neuronal information. The small world-like connectivity of the cerebral cortex allows for global computation on multiple spatial and temporal scales. The perpetual interactions among the multiple network oscillators keep cortical systems in a highly sensitive metastable state and provide energy-efficient synchronizing mechanisms via weak links. In a sequence of cycles, this book travels from the physics of oscillations through neuronal assembly organization to complex cognitive processing and memory storage.

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... beta, 12 -30 Hz; gamma, >30 Hz) (Buzsáki, 2006). Each of these bands are believed to serve specific functions. ...
... Each of these bands are believed to serve specific functions. This includes lower frequencies being more likely to facilitate long-range coordination of brain networks, whilst fast oscillations in the gamma-band are implicated in local activity and information processing (Buzsáki, 2006). We focussed on the role of the four lower frequency bands in anticipatory processes during the cue-target interval in the pro/antisaccade task. ...
... Berger, 1929). Alpha waves consist of prominent oscillations around 8-12 Hz, especially over the visual cortex when the eyes are closed (Buzsáki, 2006). However, they are also ubiquitous in the brain, and are thought to indicate "idling" or inhibition/suppression of brain regions (Händel et al., 2011). ...
Article
Deficits in cognitive control and attentional processing are commonly observed in people with Attention-Deficit/Hyperactivity Disorder (ADHD) and Specific Learning Difficulties (SpLDs) such as Dyslexia. Poorer performance in the pro/antisaccade task have been observed in these individuals, which suggests impaired visual attention and inhibitory control mechanisms. Atypical cognitive processing is also related to a state of autonomic hypoarousal in conditions such as ADHD. In this thesis, I examined whether the computer-based gaze-control RECOGNeyes training program using the pro/antisaccade task could improve cognitive control of visual attention by targeting the visual attention network and whether such improvements correlate with increased arousal. A group of 35 volunteers with SpLDs and/or ADHD completed the pro/antisaccade task before and after two weeks of training their visual attention using RECOGNeyes. Magnetoencephalography (MEG), pupillometry and electrocardiography were recorded, while they performed the pro/antisaccade task. Our task performance measures, reaction time (RT) and accuracy, and reading indices improved after RECOGNeyes training. Our findings demonstrate for the first time that autonomic measures of sympathetic pupil dilation and parasympathetic cardiac deceleration both correlate with faster saccadic RTs together (which was stronger for antisaccade trials than prosaccade trials) and account for separate variance in RT. Additionally, distinct MEG oscillatory profiles were uncovered in different frequency bands within regions of the visual attention network during the pro/antisaccade task. Slow-wave oscillations of delta and theta bands show anteriorising effects, suggested to mediate timing responses and bottom-up communication from the posterior to anterior network regions. Alpha-oscillations are proposed to have top-down preparatory inhibitory effects, particularly from the bilateral frontal eye field, and alpha-suppression in the right parietal eye field. Beta amplitude presents an additional “anticipatory” event-related desynchronisation (ERD) prior to target onset that is stronger on day 2 and antisaccade trials, which could relate to generalised inhibitory control mechanisms. This thesis supports the existence of complex central and autonomic processes underlying attention and arousal that are not yet fully understood and warrant further investigation. By increasing our understanding of the integrated attentional processes and inhibitory control, this could help the development of targeted treatment solutions, such as RECOGNeyes, for ADHD and SpLDs, to improve outcomes in these individuals.
... Traditionally, neural oscillations have been divided into specific frequency bands and studied according to their spectral features alone. 37,38 Higher-frequency oscillations (.70 Hz), known as gamma rhythms, are thought to represent local cortical ensembles. 39,40 Narrow bands under 30 Hz, such as theta (4)(5)(6)(7), alpha (8)(9)(10)(11)(12) Hz) and beta rhythms (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), have been posited to represent modulatory circuits associated with deeper grey structures such as the thalamus and hippocampus. ...
... EEG data were common average re-referenced. Frequency bands were defined as follows: theta, 4-7 Hz; alpha, 8-12 Hz; beta, 13-29 Hz; gamma, 65-100 Hz. 38 Power spectral density ...
Article
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Chronic stroke patients with upper limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain-computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain-computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm specific mechanism would correlate with the functional improvements effected by a brain-computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain-computer interface therapy is associated with alterations in phase-amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain-computer interface task for 12 weeks. Resting state EEG recordings and motor function data were acquired before initiating brain-computer interface therapy and once every four weeks after the therapy. Changes in phase-amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta-gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain-computer interface therapy sessions. Importantly, increase in theta-gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta-gamma coupling increase following brain-computer interface therapy were mostly located in the hand regions of primary motor cortex on the left and right cerebral hemispheres. Beta-gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha-gamma coupling was not altered by brain-computer interface therapy. Power spectra did not change significantly over the course of the brain-computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain-computer interface therapy was strongly correlated with increased theta-gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.
... By adjusting the oscillation amplitude, the replay frequency can be closely matched to the training frequency. In line with experimental recording (Buzsáki, 2006;Buzsáki & Draguhn, 2004), this behavior is observed for a range of physiological oscillation frequencies: alpha (10 Hz), beta (30 Hz), gamma (70 Hz). Due to the low-pass filtering of the neuronal membranes and synapses, higher oscillation frequencies have a smaller effect. ...
... We therefore propose a second, biologically more plausible type of coherent noise resulting from a random stimulus locking to an intrinsic spatiotemporal coherent activity pattern on a large spatial scale, such as waves of cortical activity. Coherent spatiotemporal activity patterns in the cortex are observed in many different forms and under various conditions, including different sleep states, but also in awake behaving animals (Buzsáki, 2006;Buzsáki & Draguhn, 2004;Sato et al., 2012;Denker et al., 2018). In such states, neighboring neurons receive coherent input with identical phase, whereas distant neurons are exposed to different phases. ...
Preprint
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various replay strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning.
... Brain waves are manifestations of synchronized neuronal currents widely used for describing neurophysiological activity (Fries, 2005;Buzsáki, 2011;Thut et al., 2012;Cannon et al., 2014). However, our understanding of these phenomena depends fundamentally on mathematical and computational tools used for analyzing the recorded Local Field Potentials (LFPs). ...
... Discrete Fourier Transform techniques currently provide the most commonly used semantics and the main framework for interpreting the structure and physiological functions of the brain waves (Roopun et al., 2008;Buzsáki, 2011;Colgin, 2016). DPT offers an alternative, high-resolution technique that leads to a novel perspective on the LFP's oscillatory component, extracted from its "noise shell." ...
Article
Full-text available
Neurons in the brain are submerged into oscillating extracellular potential produced by synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of neurophysiological activity, broadly studied in basic neuroscience and used in applications. However, our interpretation of the brain waves' structure and hence our understanding of their functions depend on the mathematical and computational approaches used for data analysis. The oscillatory nature of the wave dynamics favors Fourier methods, which have dominated the field for several decades and currently constitute the only systematic approach to brain rhythms. In the following study, we outline an alternative framework for analyzing waves of local field potentials (LFPs) and discuss a set of new structures that it uncovers: a discrete set of frequency-modulated oscillatory processes—the brain wave oscillons and their transient spectral dynamics.
... To compensate for the 1/f distribution of power across the spectrum, i.e. decreasing power with increasing frequency (Buzsáki, 2006;Milstein et al., 2009), the decomposition is first performed at several frequencies within the range of interest (e.g. 80-150 Hz). ...
... The choice of these parameters must be guided by the research questions. For example, collapsing frequencies between 10 and 120 Hz may have little interpretative value, as that may collapse different neural processes (Buzsáki, 2006). As another example, choosing narrow time-windows may compromise the analysis of lower frequency bands because of border effects. ...
Article
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Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.
... Neurophysiological signatures representing neuronal population activity are local field potential (LFP) signals, consisting of diverse oscillatory patterns that dynamically vary with attentional, motivational, arousal states, and entrain synchronous rhythmic spikes (Buzsáki, 2006). Recently, LFP oscillations in the PFC have been shown to modulate social behavior. ...
Article
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The medial prefrontal cortex and amygdala are involved in the regulation of social behavior and associated with psychiatric diseases but their detailed neurophysiological mechanisms at a network level remain unclear. We recorded local field potentials (LFPs) from the dorsal medial prefrontal cortex (dmPFC) and basolateral amygdala (BLA) while male mice engaged on social behavior. We found that in wild-type mice, both the dmPFC and BLA increased 4–7 Hz oscillation power and decreased 30–60 Hz power when they needed to attend to another target mouse. In mouse models with reduced social interactions, dmPFC 4–7 Hz power further increased especially when they exhibited social avoidance behavior. In contrast, dmPFC and BLA decreased 4–7 Hz power when wild-type mice socially approached a target mouse. Frequency-specific optogenetic manipulations replicating social approach-related LFP patterns restored social interaction behavior in socially deficient mice. These results demonstrate a neurophysiological substrate of the prefrontal cortex and amygdala related to social behavior and provide a unified pathophysiological understanding of neuronal population dynamics underlying social behavioral deficits.
... We so far focused on the ongoing activity's spatial neural code-how about its temporal neural code? The brain is characterized by a complex temporal organization that includes a wide range of regular faster frequencies (1-260 Hz) and more irregular slower frequencies (0.001-1 Hz) (Buzsáki 2006 Remarkably, the brain's temporal organization follows a certain spatial pattern as lower-order unimodal sensory regions display shorter timescales while higher-order transmodal regions as of the default-mode network and other higher-order networks (like central executive network) exhibit longer timescales ( Interestingly, strong similarities in the unimodal and transmodal cortical distribution of intrinsic neural timescales have been observed on cellular levels in monkeys (Cirillo and others 2018; Chaudhuri and others 2015; Murray and others 2014) and mice (Fulcher and others 2019) and regional-network levels in human EEG/MEG and fMRI (Demirtaş and others 2019; Golesorkhi and others 2021a, 2021b; Ito and others 2020; Raut and others 2020; see below for details). Therefore, the concept of intrinsic neural timescales may offer a unifying principle for linking cellular (Murray and others 2014), population (Runyan and others 2017), and regional/network (Golesorkhi and others 2021a, 2021b; Ito and others 2020; Raut and others 2020) levels of activity (see Marom 2010). ...
Article
What is the role of the brain’s ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a “dual model” of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle’s original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the “baseline model.” Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain’s rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain’s ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain’s rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.
... In our study, in two coupled neurons we found long transient processes and regimes emerging due to nonstationarity of the electronic components. If such regimes can appear near a bifurcation line in a considered simple scheme of two neurons, they (or some similar ones) are very likely to appear in much more complex networks, and therefore, can be considered as valid models of brain dynamics for some normal regimes like sleep and passive wakefulness in which generalized synchronization of large areas is not established and oscillation activity patterns often replace each other [37,38]. ...
Article
The new version of electronic implementation of FitzHugh–Nagumo neuron model was proposed together with a new circuit for synapse (sigmoid activation function). The proposed neuron and synapse models provide better representation of activation function in biological neurons including possibility to model excitatory and inhibitory connections. Various regimes in two FitzHugh–Nagumo neurons were studied numerically and in hardware experiment. Different scenarios of oscillation emergence were investigated, including saddle-node cycle bifurcation leading to appearance of highly nonlinear limit cycles of large amplitude. Long living transients near these bifurcations were found those are of particular interest for modeling some metastable phenomena in living systems like sleep and epilepsy.
... Val/Val allele carriers showed lower EEG power compared to Met/Met homozygotes within the upper alpha range and it was correlated with to executive performance in wakefulness in healthy young men [40]. Although many studies have focused on determining the generation of EEG oscillation [62] very little is known about the genes underling distinct EEG traits. Twin studies emphasize the importance of genetic influences on patterns of spontaneous resting EEG [63]. ...
Chapter
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Introduction. A common functional polymorphism in the gene that codes for Catechol-O-methyltransferase (COMT) is associated with altered executive performance. Electrophysiological markers indicate that this COMT polymorphism affects prefrontal cognition. Behavioral data obtained from middle-aged female carriers of the Val allele showed better performance than carriers of the Met allele in executive processes. The impact on brain function, however, is unknown. The aim of the current study was to examine the effects of the COMT gene polymorphism on brain function in postmenopausal women at rest and during executive performance. Method. EEG recordings were conducted at baseline (BL) and during the performance of a prefrontal function task in healthy postmenopausal carriers of the Val allele (n = 13) and Met allele (n = 11) carriers. The EEG spectral power in the frontal, central, parietal and occipital areas was analyzed during BL and task conditions. The number of categories completed, and correct responses (trials) as well as perseverative errors and errors of task were evaluated. Results. Participants with the Val/Val genotype performed better in the prefrontal function task. At BL, Val carriers showed lower delta, theta, alpha1, alpha2 and beta1power when compared to the Met carriers. When comparing BL to task, the Val carriers showed increased power in the delta, alpha1 and alpha2 bands in frontal region, greatly increased power in theta band in the all brain regions and increased power in the beta1and beta2 bands in the central and parietal regions. In contrast, Met carriers showed increased power in the delta and theta bands in the all brain regions, attenuated power in the alpha band, increased power in the beta1band in the frontal region and increased power in the beta2 band in the central and parietal regions. Conclusion. These findings show that during executive processing, Val carriers have increased power in the delta, theta and alpha bands in the frontal region and was associated with better performance, whereas Met allele carriers have increased power in the delta and beta2 bands in all regions and was associated with poor performance. These differences suggest that mechanisms involving dopamine contribute to EEG oscillations at rest and during executive processing.
... Neuroscience becomes the recent research hotspot that mainly reveals the biophysical mechanism in brain, and electrophysiological study is performed to bridge the relationship between physiological activities and electrical responses of neurons. Understanding how single neurons communicate and contribute with each other in the large neuron network is still a big challenge in neuroscience [253][254][255]. Conventionally, the action potential or ion channel current of signal neuron recorded by single electrode (e.g., patchclamp) is the gold standard to explore the neurophysiology [256][257][258][259][260]. The patch-clamp technique forms a high impedance seal between glass micropipette and cell membrane by vacuum. ...
Article
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Developing techniques to effectively and real-time monitor and regulate the interior environment of biological objects is significantly important for many biomedical engineering and scientific applications, including drug delivery, electrophysiological recording and regulation of intracellular activities. Semi-implantable bioelectronics is currently a hot spot in biomedical engineering research area, because it not only meets the increasing technical demands for precise detection or regulation of biological activities, but also provides a desirable platform for externally incorporating complex functionalities and electronic integration. Although there is less definition and summary to distinguish it from the well-reviewed non-invasive bioelectronics and fully implantable bioelectronics, semi-implantable bioelectronics have emerged as highly unique technology to boost the development of biochips and smart wearable device. Here, we reviewed the recent progress in this field and raised the concept of “Semi-implantable bioelectronics”, summarizing the principle and strategies of semi-implantable device for cell applications and in vivo applications, discussing the typical methodologies to access to intracellular environment or in vivo environment, biosafety aspects and typical applications. This review is meaningful for understanding in-depth the design principles, materials fabrication techniques, device integration processes, cell/tissue penetration methodologies, biosafety aspects, and applications strategies that are essential to the development of future minimally invasive bioelectronics.
... The resulting periodicities might therefore be due to cyclical processing in the motor system or consciousness itself as opposed to being purely perceptual. Indeed, observation of periodicities in motor tasks suggests that these too involve some form of cyclical processing (Reimer and Hatsopoulos 2010;Buzsáki 2006). This goes against Dehaene's hypothesis that the fixed portion of the RT is due to transduction and motor response with the variable, and so periodic, portion being due to perception. ...
Article
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Each of our sensory modalities — vision, touch, taste, etc. — works on a slightly different timescale, with differing temporal resolutions and processing lag. This raises the question of how, or indeed whether, these sensory streams are co-ordinated or ‘bound’ into a coherent multisensory experience of the perceptual ‘now’. In this paper I evaluate one account of how temporal binding is achieved: the temporal windows hypothesis, concluding that, in its simplest form, this hypothesis is inadequate to capture a variety of multisensory phenomena. Rather, the evidence suggests the existence of a more complex temporal structure in which multiple overlapping windows support distinct functional mechanisms. To aid in the precise formulation of such views, I propose a taxonomy of temporal window types and their characteristics that in turn suggests promising avenues for future empirical and philosophical research. I conclude by examining some philosophical implications of multi-window models for the metaphysics of perception and perceptual experience more generally.
... Most are at such a low level that the human ear will not be able to perceive them as tones. Nevertheless, these frequencies are so basic and so complex that Buzsáki (2006) and others believe the brain is characterized by an incessant cacophony. ...
Chapter
The question pursued in this chapter is if sound itself has impact on meaning. It is built on a study from 2013 about the impact of rhymes. The assumption was that rhyme itself does not convey any significant information. This was falsified. The participants considered different slogans, with and without rhymes. The study demonstrated that rhymes strengthened the slogans’ flow, easiness to remember and the feeling, but rhymes made them also more truthful and more likely to follow. Based on this, the way The Coca-Cola Company has made us associate a soft-drink with joy, partying and fun underline the same type of irrationality. This is explained by synesthesia as providing the synthesizing mental process, whereas the conventionally given rule-systems provide analytical distinctions.
... Following the discovery of brain rhythms less than century ago, many hypotheses concerning possible functions of neural synchronies and oscillations have been proposed and debated (Walter, 1959a,b;John, 1967b;Thatcher and John, 1977;Basar, 1988;Buzsáki, 2006;Nunez and Srinivasan, 2006;Uhlhaas et al., 2009;Klimesch, 2012;Singer, 2018Singer, , 2021. These include: ...
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.
... However, a common methodological constraint is the use of predefined frequency bands and/or regions of interest. While it is a common practice to classify oscillatory activity into bands, this is not optimal since the borders between canonical frequency ranges have been arbitrarily drawn and, consequently, oscillations arising from the same physiological machinery in different species, at different ages, or even in the same individual across states are often labelled as different rhythms ( Boersma et al., 2011 ;Buzsáki, 2006 ;Buzsáki et al., 2013 ). Similarly, in most studies the brain is parcellated into predefined regions of interest based on an anatomical atlas, which does not necessarily have to overlap with the organisation of the brain based on its functional properties ( Eickhoff et al., 2018 ). ...
Article
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Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results provided empirical confirmation of the canonical frequency bands and revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Critically, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.
... Parts 2 and 6 of the case could be used to introduce ensemble networks and rhythmic activity (Buzsáki 2006) and/or different analytical approaches applied to single and multi-unit electrophysiological data. Students could learn to code using student-generated electrophysiology data (Fink, 2017) or big, open-source electrophysiology data like the Allen Cell Types Database (http://celltypes.brain-map.org; ...
Article
A fictitious patient, Miguel, has been diagnosed with drug-resistant epilepsy and is awaiting neurosurgery. While in the hospital, Miguel agrees to participate in a research study in which depth electrodes are used to record neuronal activity in response to a range of stimuli. Interestingly, a neuron is identified that seems to respond selectively to video clips of the animated satirical TV show The Simpsons. Students are challenged to make observations, formulate and revise hypotheses, and interpret data, excerpted from an authentic dataset derived from actual patients in a 2008 Science paper. Students then consider implications for these data, evaluate their ability to generalize to non-human (rodent) models, and speculate about future directions for this research. Adaptations of this case have been implemented in introductory and advanced neuroscience courses. Students responded positively to the case, and reported gains in science competence and identity, particularly in the introductory courses. Suggestions for implementation and adaptation of this experience are offered. While this case has been implemented in undergraduate neuroscience courses, it might also be used in physiology, psychology, biology, research methods, or clinical courses.
... Here, this is implemented by lowering the somatic spike threshold θ E of the excitatory neurons. In the biological system, this increase in excitability could, for example, be caused by the effect of neuromodulators [40,41], additional excitatory inputs from other brain regions implementing a top-down control, e.g, attention [42,43], or propagating waves during sleep [44,45]. ...
Article
Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.
... Neurons interact via electrochemical signals resulting in the flow of currents through synapses. Rhythmic voltage fluctuations evolve through synchronized neuronal electrical activity within neuronal networks (Buzsáki, 2006). These so-called neuronal oscillations follow synchronized fluctuations in neuronal excitability and are generated by both cortical and subcortical brain areas. ...
Article
Background Deep Brain Stimulation (DBS) of the Medial Forebrain Bundle (MFB) induces antidepressant effects both clinically and pre-clinically. However, the acute electrophysiological changes induced by MFB DBS remain unknown. Objective The study investigated acute mfb DBS effects on neuronal oscillations in distinct neuronal populations implicated in the pathophysiology of depression. Methods The Flinders Sensitive Line (FSL) rodent depression model and Sprague-Dawley (SD) controls were used in the study. Recording electrodes were implanted unilaterally in the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), ventral tegmental area (VTA); DBS electrodes were implanted bilaterally in the mfb. The FSL Stim and SD Stim received bilateral mfb DBS, whereas the FSL Sham and SD Shams were not stimulated. Local field potentials (LFPs) from all areas were recorded at baseline, during, and post stimulation. Neuronal oscillations were analyzed. Results mfb DBS induced 1) a significant increase of low gamma (30–45 Hz) oscillations in the mPFC uniquely in FSLs; 2) a significant increase of low gamma oscillations in the NAc and VTA in SDs and FSLs; and 3) an increase in the expression of Gad1 in the mPFC of FSL and SDs, while only increasing the expression in the NAc of FSLs. Conclusion mfb DBS differentially affected neuronal oscillations in the mPFC, NAc and VTA across SD and FSL rats. Low gamma oscillations rose significantly in the mPFC of FSL rats. Molecular analysis points to a mechanism involving GABAergic interneurons as they regulate low gamma oscillations.
... LFPs can be measured at the scalp using electroencephalography, and as they are continuously present can be organized by frequency (in Hz): slow, < 1; δ, 1-4; θ, 5-8, α, 9-12; α, 13-25; γ, 26-80 (172). It is thought that the complex interplay of these rhythms is the basis for attention and cognition (173,174), that when disrupted, results in disorders in thought and attention (175). These rhythms can be observed using multiple approaches, including magnetoencephalography (MEG) (176). ...
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The phencyclidine-derivative ketamine [2-(2-chlorophenyl)-2-(methylamino)cyclohexan-1-one] was added to the World Health Organization's Model List of Essential Medicines in 1985 and is also on the Model List of Essential Medicines for Children due to its efficacy and safety as an intravenous anesthetic. In sub-anesthetic doses, ketamine is an effective analgesic for the treatment of acute pain (such as may occur in the perioperative setting). Additionally, ketamine may have efficacy in relieving some forms of chronic pain. In 2019, Janssen Pharmaceuticals received regulatory-approval in both the United States and Europe for use of the S-enantiomer of ketamine in adults living with treatment-resistant major depressive disorder. Pre-existing anxiety/depression and the severity of postoperative pain are risk factors for development of chronic postsurgical pain. An important question is whether short-term administration of ketamine can prevent the conversion of acute postsurgical pain to chronic postsurgical pain. Here, we have reviewed ketamine's effects on the biopsychological processes underlying pain perception and affective mood disorders, focusing on non-NMDA receptor-mediated effects, with an emphasis on results from human trials where available.
... The EEG recordings often investigate the information associated with the frequency domain, since it shows the brain presents specific frequency ranges, called rhythms, associated with specific regions and conditions. By convention, they are defined as Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (>30 Hz) (Buzsáki 2009). For spontaneous EEG recordings, the neural oscillatory activity is better represented by these frequency bands than by amplitude over time. ...
Book
Collection of selected papers submitted and presented at the III Latin American Workshop on Computational Neuroscience (LAWCN'21), held in the city of São Luís do Maranhão, Brazil, from 8 to 10th December 2021. Papers have been peer-reviewed and selected for their superior quality and impact. Topics covered are within the areas of Computational Neuroscience, Artificial Intelligence, and Neuroengineering.
... The importance of the brainstem discussed here is not intended to undermine the role of the cortex, but rather highlight an oftenundervalued aspect of neuroanatomy (Winn, 2012). The complex relationship between the brainstem its wider neuroanatomy underpins the wider role played by this core region (both in terms of location and function) in core aspects of human experience (Buzsáki, 2006;Damásio, 2010;Panksepp & Biven, 2012). ...
Article
We review evidence of non-verbal, embodied narratives in human infancy to better understand their form and function as generators of common experience, regulation, and learning. We examine their development prior to the onset of language, with a view to improve understanding of narrative as regular motifs or schemas of early experience in both solitary and social engagement. Embodied narratives are composed of regular patterns of interest, arousal, affect, and intention that yield a characteristic four-part structure of (i) introduction, (ii) development, (iii) climax, and (iv) resolution. Made with others these form co-created shared acts of meaning, and are parsed in time with discreet beginnings and endings that allow a regular pattern to frame and give predictive understanding for prospective regulation (especially important within social contexts) that safely returns to baseline again. This characteristic pattern, co-created between infant and adult from the beginning of life, allows the infant to contribute to, and learn, the patterns of its culture. We conclude with a view on commonalities and differences of co-created narrative in non-human primates, and discuss implications of disruption to narrative co-creation for developmental psychopathology.
... Nevertheless, we acknowledge that the iEEG signal is very rich, and that examining amplitude changes in specific frequency bands is far from the only way to analyze this signal. In particular, while the BGA appears to reflect the local responses of a population of neurons, the low frequency oscillations (i.e., theta, alpha, and beta) are thought to serve as carrier frequencies used by distant nodes within large-scale networks to communicate (Buzsáki, 2006). Thus, other analyses, such as measuring the coupling between the phase of slow oscillations and the power of higher frequencies (notably the BGA) or measuring the phase coupling between two oscillatory rhythms could also be performed to inform important aspects of the functional dy-namics of brain activity that we have not addressed in this work, such as the directionality of information flow through a network (Canolty & Knight, 2010). ...
Thesis
Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. In this thesis, we investigated how brain activity partly explain choice variability during a multi-attribute choice task by taking advantage of the rare opportunity to either directly record intracortical activity in the human brain or to perform intracortical stimulation to probe the causal involvement of key cortical regions. In the first study, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of participants (n = 30), as they performed interleaved quiz and choice tasks. Baseline neural activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedback received in the quiz task, and then with the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) respectively signaled periods of high and low mood, and 2) increased BGA in vmPFC and daIns respectively promoted and tempered risk-taking by overweighting gain versus loss prospects. Thus, incidental feedback induces brain states that correspond to different moods and biases the comparison of safe and risky options. More generally, this first study might explain why people experiencing positive (or negative) outcomes in some part of their lives tend to expect success (or failure) in any other. In the second study, we focused on the neuro-anatomical correlates underlying the effects of visual fixations on multi-attribute choices. Intracerebral EEG data were collected simultaneously with gaze data in a large group of participants (n = 38), as they performed a multi-attribute accept/reject choice task. Results from study 2 showed that 1) gaze-dependent neural activity (BGA) correlated positively with the value of a given attribute when fixated and negatively with the attribute’s value when not fixated in a large brain network, 2) gaze-dependent neural activity in the vmPFC positively predicted subjects’ choices when looking at gains and 3) gaze-dependent neural activity in the aINS negatively predicted subjects’ choices when looking at losses. Thus, our findings specify key neuro-anatomical insights into how gaze pattern interferes with neural activity to bias multi-attribute choices. Lastly, in the third empirical study of this thesis, we investigated the effect of targeted disruption of anterior insular cortex and ventromedial prefrontal cortex on risky choices. The effect of intracranial electrical stimulation (iES) delivered directly to the human cortex at 50 Hz in a group of epileptic patients (n = 13) were examined while the subjects performed a choice task similar to that used in the previous two studies. Results showed a functional dissociation within the anterior insula: iES on the dorsal anterior insula (daIns) increased risky choices whereas iES on the ventral anterior insula (vaIns) promoted safer choices. Conversely, intracranial electrical stimulation on the vmPFC tended to promote risk-taking (as in daIns). These rare cases highlight the potential causal importance of these brain areas during multi-attribute choices involving uncertainty and provide clues for future mechanistic studies of the anatomy and physiology of choices under uncertainty. Overall, this PhD has expanded knowledge of the neurocomputational mechanisms underlying multi-attribute choice by suggesting that dissociable brain systems may be involved in representing the value of appetitive vs. aversive attributes both prior to and during the choice process.
... The rationale is that the oscillator states are believed to represent periodicity in a short-time integration of neuronal population spike-rates; this periodicity is likely to be band-limited due to intrinsic timeconstants of the relevant neural circuits and neurophysiology. A reasonable candidate band is theta, which ranges from about 3-8 Hz (Buzsáki and Draguhn, 2004;Buzsaki, 2006), or The coupled oscillators model in the TiR framework. Periodic TiRs θ1, θ2, and θ3 are phase coupled as indicated by +/-symbols. ...
Article
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A conceptual framework and mathematical model of the control of articulatory timing are presented, in which feedback systems play a fundamental role. The model applies both to relatively small timescales, such as within syllables, and to relatively large timescales, such as multi-phrase utterances. A crucial distinction is drawn between internal/predictive feedback and external/sensory feedback. It is argued that speakers modulate attention to feedback to speed up and slow down speech. A number of theoretical implications of the framework are discussed, including consequences for the understanding of syllable structure and prosodic phrase organization.
... The EEG recordings often investigate the information associated with the frequency domain, since it shows the brain presents specific frequency ranges, called rhythms, associated with specific regions and conditions. By convention, they are defined as Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12-30 Hz), and Gamma (>30 Hz) (Buzsáki 2009). For spontaneous EEG recordings, the neural oscillatory activity is better represented by these frequency bands than by amplitude over time. ...
... It has been proposed that the functional and anatomical connections between auditory and motor-related areas allow entrainment induced by periodic auditory stimuli to modulate the activity of a distributed network of motor and sensory structures (Buzsáki, 2009;Large et al., 2015;Thaut et al., 2015; for review, see Damm et al., 2020). Psychophysical and brain imaging investigations into rhythmic auditory-motor entrainment have shown extremely fast and temporally precise auditory projections into the motor system, entraining motor responses even below thresholds of conscious awareness and engaging complex corticocerebellar networks (Thaut et al., 1999(Thaut et al., , 2009Roberts et al., 2000;Stephan et al., 2002;Thaut and Kenyon, 2003). ...
... Различните невронни трептения в рамките на специфични честотни диапазони служат за различни когнитивни или двигателни операции. Например нервните трептения в бетадиапазона често са свързани с вниманието, докато тетаритмите са свързани с паметта (Buzsáki, 2006). По този начин човек може да си представи ЕЕГ честотния спектър, използвайки аналогията на разпределението на честотата на разпространение на радиосигнали. ...
... Whether and to what extent frequency differences shape information flow and neural computation in the brain in a causal manner remains to be established. Demonstrating that a particular oscillation is necessary for a neural computation is difficult because experimentally removing an oscillation without affecting various other network properties is normally impossible, given that oscillations are usually emergent network phenomena (Buzsáki, 2006). Nevertheless, this review demonstrates that changes of a few Hz either between brain locations or a moment-to-moment basis, according to stimulus or cognitive conditions, is a property of neuronal oscillations in many frequency bands. ...
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Brain oscillations emerge during sensory and cognitive processes and have been classified into different frequency bands. Yet, even within the same frequency band and between nearby brain locations, the exact frequencies of brain oscillations can differ. These frequency differences (detuning) have been largely ignored and play little role in current functional theories of brain oscillations. This contrasts with the crucial role that detuning plays in synchronization theory, as originally derived in physical systems. Here, we propose that detuning is equally important to understand synchronization in biological systems. Detuning is a critical control parameter in synchronization, which is not only important in shaping phase-locking, but also in establishing preferred phase relations between oscillators. We review recent evidence that frequency differences between brain locations are ubiquitous and essential in shaping temporal neural coordination. With the rise of powerful experimental techniques to probe brain oscillations, the contributions of exact frequency and detuning across neural circuits will become increasingly clear and will play a key part in developing a new understanding of the role of oscillations in brain function.
... Other studies report task-related synchronization between theta oscillation of the mPFC and vHPC [16]. In addition to the theta-gamma coupling, another coupling between theta rhythm and high-frequency oscillations (HFOs) falling within 110 Hz and 160 Hz, has been reported in several studies [26,[40][41][42][43][44][45]. Task-related dynamic PAC within the hippocampus and across the hippocampus and other Paxinos and Watson, 2007) show the section at 3.24 mm AP. ...
Article
Interaction of oscillatory rhythms at different frequencies is considered to provide a neuronal mechanism for information processing and transmission. These interactions have been suggested to have a vital role in cognitive functions such as working memory and decision-making. Here, we investigated the medial prefrontal cortex (mPFC), which is known to have a critical role in successful execution of spatial working memory tasks. We recorded local field potential oscillations from mPFC while rats performed a delayed-non-match-to-place (DNMTP) task. In the DNMTP task, the rat needed to decide actively about the pathway based on the information remembered in the first phase of each trial. Our analysis revealed a dynamic phase-amplitude coupling (PAC) between theta and high frequency oscillations (HFOs). This dynamic coupling emerged near the turning point and diminished afterward. Further, theta activity during the delay period, which is thought of as the maintenance phase, in the absence of the coupling, can predict task completion time. We previously reported diminished rat performance in the DNMTP task in response to electromagnetic radiation. Here, we report an increase in the theta rhythm during delay activity besides diminishing the coupling after electromagnetic radiation. These findings suggest that the different roles of the mPFC in working memory could be supported by separate mechanisms: Theta activity during the delay period for information maintenance and theta-HFOs phase-amplitude coupling relating to the decision-making procedure.
Chapter
This chapter introduces the three basic concepts of biological time: Evolutionary time; Developmental time; and Rhythmic or Cycling time. The latter is the main focus of this text and involves the evolution of biological rhythms. Initially, we will explore two fundamental reasons for the existence of these biological rhythms. The first lies in the need for any complex goal-driven device to be able to organize and order the various activities in which the device engages. This is not unique to biological organisms but is a more general principle applicable to any device, evolved or designed. The second reason lies in the nature of the Earth’s environment and the role played by the many geophysical cycles in changing environmental conditions. Daily and seasonal cycles generate large environmental changes, and the ability of organisms to adapt to these temporal cycles will determine an organism’s evolutionary success or failure. As we investigate these ideas, we will discover that the very nature of physiological systems makes them prone to rhythmicity and oscillation. We will also discover that the close linkages between biological systems at every level, from biochemical process to complex ecosystem, generates a temporal cascade in which a significant oscillation at one level of a biological system promotes oscillations at all other levels. Finally, the concept of mathematical modeling of biological systems is introduced as a method of checking hypotheses and assumptions.
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Interpersonal coordination requires precise actions concerted in space and time in a self-organized manner. We found, using soccer teams as a testing ground, that a common timeframe provided by adequate acoustic stimuli improves the interplay between teammates. We provide quantitative evidence that the connectivity between teammates and the scoring rate of male soccer teams improve significantly when playing under the influence of an appropriate acoustic environment. Unexpectedly, female teams do not show any improvement under the same experimental conditions. We show by follow-up experiments that the acoustic rhythm modulates the attention level of the participants with a pronounced tempo preference and a marked gender difference in the preferred tempo. These results lead to a consistent explanation in terms of the dynamical system theory, nonlinear resonances, and dynamic attention theory, which may illuminate generic mechanisms of the brain dynamics and may have an impact on the design of novel training strategies in team sports.
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We present a model connecting eye movements and cortical state. Its structure includes simulated retinal images, motion detection, feature detectors and layers of spiking neurons. The designed scheme shows how the effect of micro-saccadic scale eye movements can lead to successful figure segregation in a figure-ground paradigm, by inducing changes in the neural dynamics through the time evolution of the inhibition range.
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It is common to distinguish between “holist” and “reductionist” views of brain function, where the former envisions the brain as functioning as an indivisible unit and the latter as a collection of distinct units that serve different functions. Opposing reductionism, a number of researchers have pointed out that cortical network architecture does not respect functional boundaries, and the neuroanatomist V. Braitenberg proposed to understand the cerebral cortex as a “great mixing machine” of neuronal activity from sensory inputs, motor commands, and intrinsically generated processes. In this paper, we offer a contextualization of Braitenberg’s point, and we review evidence for the interactions of neuronal activity from multiple sensory inputs and intrinsic neuronal processes in the cerebral cortex. We focus on new insights from studies on audiovisual interactions and on the influence of respiration on brain functions, which do not seem to align well with “reductionist” views of areal functional boundaries. Instead, they indicate that functional boundaries are fuzzy and context dependent. In addition, we discuss the relevance of the influence of sensory, proprioceptive, and interoceptive signals on cortical activity for understanding brain-body interactions, highlight some of the consequences of these new insights for debates on embodied cognition, and offer some suggestions for future studies.
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The present study uses EEG time-frequency representations (TFRs) with a Flanker task to investigate if and how individual differences in bilingual language experience modulate neurocognitive outcomes (oscillatory dynamics) in two bilingual group types: late bilinguals (L2 learners) and early bilinguals (heritage speakers—HSs). TFRs were computed for both incongruent and congruent trials. The difference between the two (Flanker effect vis-à-vis cognitive interference) was then (1) compared between the HSs and the L2 learners, (2) modeled as a function of individual differences with bilingual experience within each group separately and (3) probed for its potential (a)symmetry between brain and behavioral data. We found no differences at the behavioral and neural levels for the between-groups comparisons. However, oscillatory dynamics (mainly theta increase and alpha suppression) of inhibition and cognitive control were found to be modulated by individual differences in bilingual language experience, albeit distinctly within each bilingual group. While the results indicate adaptations toward differential brain recruitment in line with bilingual language experience variation overall, this does not manifest uniformly. Rather, earlier versus later onset to bilingualism—the bilingual type—seems to constitute an independent qualifier to how individual differences play out.
Article
Brain rhythms emerge from synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the PRC and developing a systematic phase reduction theory for large-scale brain rhythms remains an outstanding challenge. Here we present a theoretical framework and methodology to compute the PRC of generic spiking networks with emergent collective oscillations. We adopt a renewal approach where neurons are described by the time since their last action potential, a description that can reproduce the dynamical feature of many cell types. For a sufficiently large number of neurons, the network dynamics are well captured by a continuity equation known as the refractory density equation. We develop an adjoint method for this equation giving a semi-analytical expression of the infinitesimal PRC. We confirm the validity of our framework for specific examples of neural networks. Our theoretical framework can link key biological properties at the individual neuron scale and the macroscopic oscillatory network properties. Beyond spiking networks, the approach is applicable to a broad class of systems that can be described by renewal processes.
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Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural circuits. The Communication Through Coherence (CTC) theory proposes that oscillations reflect rhythmic changes in excitability of the neuronal populations. Thus, populations need to be properly phase-locked so that input volleys arrive at the peaks of excitability of the receiving population to communicate effectively. Here, we present a modeling study to explore synchronization between neuronal circuits connected with unidirectional projections. We consider an Excitatory-Inhibitory (E-I) network of quadratic integrate-and-fire neurons modeling a Pyramidal-Interneuronal Network Gamma (PING) rhythm. The network receives an external periodic input from either one or two sources, simulating the inputs from other oscillating neural groups. We use recently developed mean-field models which provide an exact description of the macroscopic activity of the spiking network. This low-dimensional mean field model allows us to use tools from bifurcation theory to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We identify the conditions for optimal phase-locking and effective communication. We find that inputs with high coherence can entrain the network for a wider range of frequencies. Besides, faster oscillatory inputs than the intrinsic network gamma cycle show more effective communication than inputs with similar frequency. Our analysis further shows that the entrainment of the network by inputs with higher frequency is more robust to distractors, thus giving them an advantage to entrain the network and communicate effectively. Finally, we show that pulsatile inputs can switch between attended inputs in selective attention.
Chapter
In Chapters 1–6, arguments were presented which support the twin conclusions that: 1. biological systems are inherently rhythmic and 2. the temporal structure of organisms is linked to prominent geophysical cycles. The data and evidence provided gave ample theoretical and empirical support to these arguments and established that human beings are similar to all other organisms that have evolved within earth’s time-varying environment. The ubiquitous nature of biological cycles, however, can create difficulties in analysis and understanding. With so many cycles, displaying different frequencies, waveforms, and serving different functions, how are we to make sense of it all?
Article
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Hippocampal CA2 supports social memory and encodes information about social experiences. Our previous study showed that CA2 place cells responded specifically to social stimuli (Nat Commun, (Alexander et al. 2016)). In addition, a prior study showed that activation of CA2 induces slow gamma rhythms (~ 25–55 Hz) in the hippocampus (Elife, (Alexander 2018)). Together, these results raise the question of whether slow gamma rhythms coordinate CA2 activity during social information processing. We hypothesized that slow gamma would be associated with transmission of social memories from CA2 to CA1, perhaps to integrate information across regions or promote social memory retrieval. We recorded local field potentials from hippocampal subfields CA1, CA2, and CA3 of 4 rats performing a social exploration task. We analyzed the activity of theta, slow gamma, and fast gamma rhythms, as well as sharp wave-ripples (SWRs), within each subfield. We assessed interactions between subfields during social exploration sessions and during presumed social memory retrieval in post-social exploration sessions. We found that CA2 slow gamma rhythms increased during social interactions but not during non-social exploration. CA2–CA1 theta-show gamma coupling was enhanced during social exploration. Furthermore, CA1 slow gamma rhythms and SWRs were associated with presumed social memory retrieval. In conclusion, these results suggest that CA2–CA1 interactions via slow gamma rhythms occur during social memory encoding, and CA1 slow gamma is associated with retrieval of social experience.
Chapter
This chapter examines if musical polyphony interacts with our ability to make complexity meaningful. Noam Chomsky is also important to musicology, especially through the Generative Theory of Tonal Music (GTTM). Wellformedness does not allow ambiguity. Thus GTTM corresponds to older theories of form where linguistics formed the framework, and neither have much space for the complexity that is embedded in polyphony. Heinrich Schenker contrasts this. His fundamental structure let a short two-part polyphonic sequence demonstrates how polyphonic aspects cannot be reduced to unanimity. Musical cultures without polyphony are rare. The enjoyment of complexity in polyphonic music demonstrates our ability to make an almost unmanageable complexity meaningful.
Article
Since multimodal emotion classification in different human states has rarely been studied, this paper explores the emotional mechanisms of the brain functional connectivity networks after emotional stimulation. We devise a multimodal emotion classification method fusing a brain functional connectivity network based on electroencephalography (EEG) and eye gaze (ECFCEG) to study emotional mechanisms. First, the nonlinear phase lag index (PLI) and phase-locked value (PLV) are calculated to construct the multiband brain functional connectivity networks, which are then converted into binary brain networks, and the seven features of the binary brain networks are extracted. At the same time, the features of the eye gaze signals are extracted. Then, a fusion algorithm called kernel canonical correlation analysis, based on feature level and randomization (FRKCCA), is executed for feature-level fusion (FLF) of brain functional connectivity networks and eye gaze. Finally, support vector machines (SVMs) are utilized to classify positive and negative emotions in multiple frequency bands with single modal features and multimodal features. The experimental results demonstrate that multimodal complementary representation properties can effectively improve the accuracy of emotion classification, achieving a classification accuracy of 91.32±1.81%. The classification accuracy of pupil diameter in the valence dimension is higher than that of additional features. In addition, the average emotion classification effect of the valence dimension is preferable to that of arousal. Our findings demonstrate that the brain functional connectivity networks of the right brain exhibit a deficiency. In particular, the information processing ability of the right temporal (RT) and right posterior (RP) regions is weak in the low frequency after emotional stimulation; Conversely, phase synchronization of the brain functional connectivity networks based on PLI is stronger than that of PLV.
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This study integrates two lines of research: technologies as tools and technologies as social beings, under the theoretical framework of dynamic systems, to investigate the reciprocal dynamics between functional use and relational use of artificial intelligence (AI) voice assistants, and the mediating roles of self-disclosure and privacy concerns. A two-wave longitudinal survey was conducted among 354 AI voice assistant users across 2 months. Factor analysis results supported the conceptualization and operationalization of functional use and relational use of voice assistants. Results from the cross-lagged panel model confirmed that functional use and relational use reinforced themselves over time, respectively. Relational use increased subsequent functional use, and relational use reinforced itself through self-disclosure. Surprisingly, functional use did not increase subsequent relational use; instead, longitudinal mediation analysis showed that functional use reduced subsequent relational use due to the lack of self-disclosure. Furthermore, while self-disclosure increased subsequent privacy concerns, privacy concerns did not reduce subsequent self-disclosure.
Conference Paper
It is principal to understand the interface between the built environment and its users in framing the emotional, mental and physical well-being. For the built environment to support well-being, this human-centered approach must integrate an understanding of the interplay between architectural space and the physiological and psychological processes that underly behavioral and mental functions. In this contribution, we draw a qualified approach by bridging brain, body, environment through ‘affordances’, namely the fit between the physical structure of the body and the potential possibilities for movement and interaction with the environment. Starting in homeostasis, we link affordances with sensorimotor brain dynamics, on which cognitive processes are based, to propose a biologically plausible view of human well-being through the design of the built environment. Any living organism must keep its internal balance within narrow bounds compared to the fluctuations of the environment through an interactive relationship with its environment. Equilibrium can be achieved either by adjusting the internal physiological system according to the sensed environment or by moving to a better environment. Human beings hold the capability to predict both enhancements and threats towards homeostasis, and adjust behavior according to architectural affordances. Thus, the entangled sensorimotor coupling becomes a critical feature for emotional, mental, and physical balances. As architecture determines the bounds of possible action, it has a direct impact on the said balances. Recent evidence demonstrates that limiting the affordances during a stressful psychosocial situation enhances stress levels. Similarly, cortical measures suggest that architectural affordances are processed parallel to perceptual processes. Together, these recent insights indicate that well-being by the built environment can be facilitated through the design of architectural affordances.
Chapter
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Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence—the 4C's—and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
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The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography.
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In this paper we address the following problems and provide realistic answers to them: (1) What could be the physical substrate for subjective, phenomenal, consciousness (P-consciousness)? Our answer: the electromagnetic (EM) field generated by the movement and changes of electrical charges in the brain. (2) Is this substrate generated in some particular part of the brains of conscious entities or does it comprise the entirety of the brain/body? Our answer: a part of the thalamus in mammals, and homologous parts of other brains generates the critical EM field. (3) From whence arise the qualia experienced in P-consciousness? Our answer, the relevant EM field is “structured” by emulating in the brain the information in EM fields arising from both external (the environment) and internal (the body) sources. (4) What differentiates the P-conscious EM field from other EM fields, e.g., the flux of photons scattered from object surfaces, the EM field of an electro-magnet, or the EM fields generated in the brain that do not enter P-consciousness, such as those generated in the retina or occipital cortex, or those generated in brain areas that guide behavior through visual information in persons exhibiting “blindsight”? Our answer: living systems express a boundary between themselves and the environment, requiring them to model (coarsely emulate) information from their environment in order to control through actions, to the extent possible, the vast sea of variety in which they are immersed. This model, expressed in an EM field, is P-consciousness. The model is the best possible representation of the moment-to-moment niche-relevant (action-relevant: affordance) information an organism can generate (a Gestalt). Information that is at a lower level than niche-relevant, such as the unanalyzed retinal vector-field, is not represented in P-consciousness because it is not niche-relevant. Living organisms have sensory and other systems that have evolved to supply such information, albeit in a coarse form.
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Gamma-band activity was thought to be related to several high-level cognitive functions, and Gamma ENtrainment Using Sensory stimulation (GENUS, 40 Hz sensory combined visual and auditory stimulation) was found to have positive effects on patients with Alzheimer’s dementia. Other studies found, however, that neural responses induced by single 40 Hz auditory stimulation were relatively weak. To address this, we included several new experimental conditions (sounds with sinusoidal or square wave; open-eye and closed-eye state) combined with auditory stimulation with the aim of investigating which of these induces a stronger 40 Hz neural response. We found that when participant´s eyes were closed, sounds with 40 Hz sinusoidal wave induced the strongest 40 Hz neural response in the prefrontal region compared to responses in other conditions. More interestingly, we also found there is a suppression of alpha rhythms with 40 Hz square wave sounds. Our results provide potential new methods when using auditory entrainment, which may result in a better effect in preventing cerebral atrophy and improving cognitive performance.
Research Proposal
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Dear colleagues! The proposed scientific journal is intended for specialists in medicine, management, physical medicine and rehabilitation, economics. We hope that the works presented by the authors will help to strengthen the scientific potential. Marina Pirtskhalava: Doctor of Biological Sciences, Professor, Academician, Rector of University Geomedi
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Does the structure of an adult human brain alter in response to environmental demands? Here we use whole-brain magnetic-resonance imaging to visualize learning-induced plasticity in the brains of volunteers who have learned to juggle. We find that these individuals show a transient and selective structural change in brain areas that are associated with the processing and storage of complex visual motion. This discovery of a stimulus-dependent alteration in the brain's macroscopic structure contradicts the traditionally held view that cortical plasticity is associated with functional rather than anatomical changes.
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This article contains the argument that the human ability to travel mentally in time constitutes a discontinuity between ourselves and other animals. Mental time travel comprises the mental reconstruction of personal events from the past (episodic memory) and the mental construction of possible events in the future. It is not an isolated module, but depends on the sophistication of other cognitive capacities, including self-awareness, meta-representation, mental attribution, understanding the perception-knowledge relationship, and the ability to dissociate imagined mental states from one's present mental state. These capacities are also important aspects of so-called theory of mind, and they appear to mature in children at around age 4. Furthermore, mental time travel is generative, involving the combination and recombination of familiar elements, and in this respect may have been a precursor to language. Current evidence, although indirect or based on anecdote rather than on systematic study, suggests that nonhuman animals, including the great apes, are confined to a "present" that is limited by their current drive states. In contrast, mental time travel by humans is relatively unconstrained and allows a more rapid and flexible adaptation to complex, changing environments than is afforded by instincts or conventional learning. Past and future events loom large in much of human thinking, giving rise to cultural, religious, and scientific concepts about origins, destiny, and time itself.
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By combining MEA electrophysiology with long-term time-lapse imaging, it is possible to make correlations between changes in network function and changes in neuronal morphology. By re-embodying dissociated cultured networks, network function can be mapped onto behavior, and in vitro research can now make use of a new kind of behavioral studies that include detailed (submicron) imaging not possible in vivo. By closing the sensory-motor loop around MEA cultures, they are more likely to shed light on the mechanisms of learning, memory, and information processing in animals.
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Contends that a difficulty with the connectionist idea of representation in neural networks of the mammalian brain is that a single neuron cannot make a sufficient number of connections to influence the functional organization within networks of realistic size. A hypothesis of cortico-hippocampal interaction is suggested, which involves the establishing of patterns of connectivity between the cortex and hippocampus, on the basis of temporal aspects of connectivity (i.e., axonal conduction delays) as well as spatial aspects. By means of both the available repertoire of axonal conduction delays and Hebbian processes for synaptic modification, loops of connectivity are selected which carry neural activity resonating at the frequency of the hippocampal theta rhythm. The relation between the hippocampal theta rhythm and both general behavior and learning processes is thus clarified. (PsycINFO Database Record (c) 2012 APA, all rights reserved)