scholarly journals Oscillation or not – why we can and need to know

2021 ◽  
Author(s):  
Sander van Bree ◽  
Andrea Alamia ◽  
Benedikt Zoefel

Neural oscillations are pivotal to brain function and cognition, but they can be difficult to identify. Researchers engage in careful experimentation to identify their presence, ruling out non-oscillatory processes that could give rise to a similar response. Recently, Doelling and Assaneo have argued against these efforts on the basis that oscillators are heterogeneous, which makes the line to non-oscillators blurred and thereby meaningless to draw. Here, we offer our opposing viewpoint, arguing that we can know whether oscillations are involved, and that we need to know. First, we can know because there are unique properties that only oscillators have, which can be reliably used to identify them – the line is not blurred. These unique properties include eigenfrequency, Arnold Tongue, convergence, and independence. Second, we need to know because there are shared properties, which all oscillators or all oscillators within a subclass have. These shared properties comprise all the information we get once we know there is an oscillator, including neurophysiological, functional, and methodological properties – the fruits of decades of research. We argue that identifying oscillators is crucial for the advancement of research fields as it constrains the possible neural dynamics involved and allows us to make informed predictions on a variety of levels. While neural oscillations are the start and not the end, we have to reach that start.

2020 ◽  
Vol 16 (01) ◽  
pp. 73-88
Author(s):  
Pavel Kraikivski

As put forward by neuroscientists, the mechanisms of consciousness can be elucidated by revealing correlations between neural dynamics and specific conscious percepts. Recently, I have elaborated on the mathematical formulation for a system of processes that are mutually connected to be isomorphic to a conscious percept of a point in space. Importantly, in such a system, any process can be derived through all other processes that form its complement, or “interpretation.” To generate such a solution, I am proposing a dynamical system of oscillators coupled in a manner to preserve the properties of a percept. Specifically, I crafted a dynamical system that retains the mutual relationships among processes, forming an operational map isomorphic to a distance matrix that mimics a percept of space-like properties. The study and results pave a novel way to analyze the dynamics of neural-like (oscillatory) processes with a purpose of extracting the information relevant to specific conscious percepts, which will facilitate the search for neural correlates of consciousness.


Science ◽  
2019 ◽  
Vol 366 (6465) ◽  
pp. 628-631 ◽  
Author(s):  
Nina E. Fultz ◽  
Giorgio Bonmassar ◽  
Kawin Setsompop ◽  
Robert A. Stickgold ◽  
Bruce R. Rosen ◽  
...  

Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic waste products from the brain. Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain. We discovered a coherent pattern of oscillating electrophysiological, hemodynamic, and CSF dynamics that appears during non–rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow. These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandros Gelastopoulos ◽  
Nancy J. Kopell

AbstractNeural oscillations, including rhythms in the beta1 band (12–20 Hz), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network’s behavior. We use a simplified, yet biophysical, model of a beta1 rhythm that occurs in the parietal cortex, in order to study its response to oscillatory inputs. We demonstrate that a cell has the ability to respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to that of the other. We show that this is a very general phenomenon, independent of the model used. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it.


2017 ◽  
Author(s):  
Anne Kösem ◽  
Hans Rutger Bosker ◽  
Atsuko Takashima ◽  
Antje Meyer ◽  
Ole Jensen ◽  
...  

ABSTRACTLow-frequency neural entrainment to rhythmic input has been hypothesized as a canonical mechanism that shapes sensory perception in time. Neural entrainment is deemed particularly relevant for speech analysis, as it would contribute to the extraction of discrete linguistic elements from continuous acoustic signals. Yet, its causal influence in speech perception has been difficult to establish. Here, we provide evidence that oscillations build temporal predictions about the duration of speech tokens that directly influence perception. Using magnetoencephalography (MEG), we studied neural dynamics during listening to sentences that changed in speech rate. We observed neural entrainment to preceding speech rhythms persisting for several cycles after the change in rate. The sustained entrainment was associated with changes in the perceived duration of the last word’s vowel, resulting in the perception of words with radically different meanings. These findings support oscillatory models of speech processing, suggesting that neural oscillations actively shape speech perception.


Author(s):  
Sarah Feldt ◽  
Jane X. Wang ◽  
Vaughn L. Hetrick ◽  
Joshua D. Berke ◽  
Michal Żochowski

Understanding the neural correlates of brain function is an extremely challenging task, since any cognitive process is distributed over a complex and evolving network of neurons that comprise the brain. In order to quantify observed changes in neuronal dynamics during hippocampal memory formation, we present metrics designed to detect directional interactions and the formation of functional neuronal ensembles. We apply these metrics to both experimental and model-derived data in an attempt to link anatomical network changes with observed changes in neuronal dynamics during hippocampal memory formation processes. We show that the developed model provides a consistent explanation of the anatomical network modifications that underlie the activity changes observed in the experimental data.


2021 ◽  
Vol 22 (2) ◽  
pp. 545
Author(s):  
Yoshihiko Kakinuma

Since the discovery of non-neuronal acetylcholine in the heart, this specific system has drawn scientific interest from many research fields, including cardiology, immunology, and pharmacology. This system, acquired by cardiomyocytes independent of the parasympathetic nervous system of the autonomic nervous system, helps us to understand unsolved issues in cardiac physiology and to realize that the system may be more pivotal for cardiac homeostasis than expected. However, it has been shown that the effects of this system may not be restricted to the heart, but rather extended to cover extra-cardiac organs. To this end, this system intriguingly influences brain function, specifically potentiating blood brain barrier function. Although the results reported appear to be unusual, this novel characteristic can provide us with another research interest and therapeutic application mode for central nervous system diseases. In this review, we discuss our recent studies and raise the possibility of application of this system as an adjunctive therapeutic modality.


2014 ◽  
Author(s):  
Juhan Aru ◽  
Jaan Aru ◽  
Viola Priesemann ◽  
Michael Wibral ◽  
Luiz Lana ◽  
...  

AbstractCross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.HighlightsFundamental caveats and confounds in the methodology of assessing CFC are discussed.Significant CFC can be observed without any underlying physiological coupling.Non-stationarity of a time-series leads to spectral correlations interpreted as CFC.We offer practical recommendations, which can relieve some of the current confounds.Further theoretical and experimental work is needed to ground the CFC analysis.


2020 ◽  
Vol 123 (5) ◽  
pp. 1645-1656
Author(s):  
Carson C. Chow ◽  
Yahya Karimipanah

The Wilson–Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although their iconic equations are used in various guises today, the ideas that led to their formulation and the relationship to other approaches are not well known. Here, we give a little context to some of the biological and theoretical concepts that lead to the Wilson–Cowan equations and discuss how to extend beyond them.


2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Marios G. Philiastides ◽  
Tao Tu ◽  
Paul Sajda

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.


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