scholarly journals Distinct cortical correlation structures of fractal and oscillatory neuronal activity

2020 ◽  
Author(s):  
Andrea Ibarra Chaoul ◽  
Markus Siegel
Keyword(s):  
Neuronal Activity ◽  
Large Scale ◽  
Neuronal Population ◽  
Correlation Structure ◽  
Oscillatory Activity ◽  
Source Analysis ◽  
Population Activity ◽  
Neuronal Mechanisms ◽  
Frequency Components ◽  
Cortical Regions

AbstractElectrophysiological population signals contain oscillatory and fractal (1/frequency) components. So far research has largely focused on oscillatory activity and only recently interest in fractal population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of fractal neuronal activity. To address this, we investigated fractal neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of fractal neuronal activity. We found that fractal population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of fractal activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and fractal correlation patterns. Our results suggest that correlations of fractal population activity serve as robust markers of cortical network interactions. Furthermore, our results show that fractal and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and fractal neuronal population activity.

scholarly journals Large-scale cortical correlation structure of spontaneous oscillatory activity

2012 ◽  
Vol 15 (6) ◽  
pp. 884-890 ◽  
Author(s):  
Joerg F Hipp ◽  
David J Hawellek ◽  
Maurizio Corbetta ◽  
Markus Siegel ◽  
Andreas K Engel
Keyword(s):  
Large Scale ◽  
Correlation Structure ◽  
Oscillatory Activity

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2018 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

Spontaneous Development of Synchronous Oscillatory Activity During Maturation of Cortical Networks In Vitro

2002 ◽  
Vol 88 (5) ◽  
pp. 2196-2206 ◽  
Author(s):  
Thoralf Opitz ◽  
Ana D. De Lima ◽  
Thomas Voigt
Keyword(s):  
Large Scale ◽  
Reversal Potential ◽  
Neuronal Population ◽  
Network Activity ◽  
Oscillatory Activity ◽  
Pharmacological Intervention ◽  
Resting Membrane Potential ◽  
Cortical Networks ◽  
Synchronous Network

Recent studies have focused attention on mechanisms of spontaneous large-scale wavelike activity during early development of the neocortex. In this study, we describe and characterize synchronous neuronal activity that occurs in cultured cortical networks naturally without pharmacological intervention. The synchronous activity that can be detected by means of Fluo-3 fluorescence imaging starts to develop at the beginning of the second week in culture and eventually includes the entire neuronal population about 1 wk later. A synchronous increase of [Ca2+]i in the neuronal population is associated with a burst of action potentials riding on a long-lasting depolarization recorded in a single cell. It is suggested that this depolarization results directly from synaptic current, which was comprised of at least three different components mediated by AMPA, N-methyl-d-aspartate (NMDA), and GABAA receptors. We never observed a gradually depolarizing pacemaker potential and found no evidence for a change of excitability during inter-burst periods. However, we found evidence for a period of synaptic depression after bursts. Network excitability recovers gradually over seconds from this depression that can explain the episodic nature of spontaneous network activity. Using pharmacological manipulation to investigate the propagation of activity in the network, we show that synchronous network activity depends on both glutamatergic and GABAAergic neurotransmission during a brief period. Reversal potential of GABAA receptor-mediated current was found to be significantly more positive than resting membrane potential both at 1 and 2 wk in culture, suggesting depolarizing action of GABA. However, in cultures older than 2 wk, inhibition of GABAAreceptors does not result in block of synchronous network activity but in modulation of burst width and frequency.

scholarly journals Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

2021 ◽  
Author(s):  
Thomas Pfeffer ◽  
Christian Keitel ◽  
Daniel S. Kluger ◽  
Anne Keitel ◽  
Alena Russmann ◽  
...  
Keyword(s):  
Low Frequency ◽  
Intermediate Frequency ◽  
Brain Regions ◽  
Neuronal Population ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Linear Relationships ◽  
Cortical Regions ◽  
Aperiodic Component

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has only been characterized for a few selected brain regions so far. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by linear and non-linear relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. The non-linearity resembled an inverted U-shape whereby intermediate pupil sizes coincided with maximum 8-32 Hz activity. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

Neuronal Activity of Mitral-Tufted Cells in Awake Rats During Passive and Active Odorant Stimulation

2008 ◽  
Vol 100 (1) ◽  
pp. 422-430 ◽  
Author(s):  
Romulo A. Fuentes ◽  
Marcelo I. Aguilar ◽  
María L. Aylwin ◽  
Pedro E. Maldonado
Keyword(s):  
Firing Rate ◽  
Strong Dependence ◽  
Field Potential ◽  
Temporal Patterns ◽  
Neuronal Population ◽  
Oscillatory Activity ◽  
Population Activity ◽  
Oscillatory Power ◽  
Rhythmic Discharge ◽  
Excitatory Responses

Odorants induce specific modulation of mitral/tufted (MT) cells' firing rate in the mammalian olfactory bulb (OB), inducing temporal patterns of neuronal discharge embedded in an oscillatory local field potential (LFP). While most studies have examined anesthetized animals, little is known about the firing rate and temporal patterns of OB single units and population activity in awake behaving mammals. We examined the firing rate and oscillatory activity of MT cells and LFP signals in behaving rats during two olfactory tasks: passive exposure (PE) and two-alternative (TA) choice discrimination. MT inhibitory responses are predominant in the TA task (76.5%), whereas MT excitatory responses predominate in the PE task (59.2%). Rhythmic discharge in the 12- to 100-Hz range was found in 79.0 and 68.9% of MT cells during PE and TA tasks, respectively. Most odorants presented in PE task increase rhythmic discharges at frequencies >50 Hz, whereas in TA, one of four odorants produced a modest increment <40 Hz. LFP oscillations were clearly modulated by odorants during the TA task, increasing their oscillatory power at frequencies centered at 20 Hz and decreasing power at frequencies >50 Hz. Our results indicate that firing rate responses of MT cells in awake animals are behaviorally modulated with inhibition being a prominent feature of this modulation. The occurrence of oscillatory patterns in single- and multiunitary discharge is also related to stimulation and behavioral context, while the oscillatory patterns of the neuronal population showed a strong dependence on odorant stimulation.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2019 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

scholarly journals Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

2019 ◽  
Author(s):  
Ryan C Williamson ◽  
Brent Doiron ◽  
Matt A Smith ◽  
Byron M Yu
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Common Ground ◽  
Network Models ◽  
Neuronal Population ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Population Activity ◽  
Large Scale Network ◽  
Scale Network

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

scholarly journals Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells

1998 ◽  
Vol 79 (2) ◽  
pp. 1017-1044 ◽  
Author(s):  
Kechen Zhang ◽  
Iris Ginzburg ◽  
Bruce L. McNaughton ◽  
Terrence J. Sejnowski
Keyword(s):  
Bayesian Methods ◽  
Neuronal Population ◽  
Place Cells ◽  
The Body ◽  
Unified Framework ◽  
Population Activity ◽  
Reconstruction Methods ◽  
Special Cases ◽  
Physical Variables ◽  
The Brain

Zhang, Kechen, Iris Ginzburg, Bruce L. McNaughton, and Terrence J. Sejnowski. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79: 1017–1044, 1998. Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multi-electrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.

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