scholarly journals Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

2021 ◽  
Author(s):  
Andrew J Quinn ◽  
Vitor Lopes-dos-Santos ◽  
Norden Huang ◽  
Wei-Kuang Liang ◽  
Chi-hung Juan ◽  
...  
Keyword(s):  
Instantaneous Frequency ◽  
Animal Movement ◽  
Field Potential ◽  
Real Data ◽  
Analytical Framework ◽  
Movement Speed ◽  
High Temporal Resolution ◽  
Cycle Duration ◽  
Neuronal Oscillations ◽  
Phase Alignment

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. 'Normalised shapes' can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

scholarly journals Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

2021 ◽  
Author(s):  
Andrew J. Quinn ◽  
Vítor Lopes-dos-Santos ◽  
Norden Huang ◽  
Wei-Kuang Liang ◽  
Chi-Hung Juan ◽  
...  
Keyword(s):  
Instantaneous Frequency ◽  
Animal Movement ◽  
Field Potential ◽  
Real Data ◽  
Analytical Framework ◽  
Movement Speed ◽  
High Temporal Resolution ◽  
Cycle Duration ◽  
Neuronal Oscillations ◽  
Phase Alignment

AbstractNon-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. ‘Normalised shapes’ can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

scholarly journals Understanding Harmonic Structures Through Instantaneous Frequency

2021 ◽  
Author(s):  
Marco S Fabus ◽  
Mark W Woolrich ◽  
Catherine E Warnaby ◽  
Andrew J Quinn
Keyword(s):  
Instantaneous Frequency ◽  
Field Potential ◽  
Analytic Number Theory ◽  
Real Data ◽  
Clear Understanding ◽  
Mode Decomposition ◽  
Sinusoidal Waveform ◽  
Neurophysiological Data ◽  
Definition Of ◽  
Hippocampal Theta

The analysis of harmonics and non-sinusoidal waveform shape in neurophysiological data is growing in importance. However, a precise definition of what constitutes a harmonic is lacking. In this paper, we propose a rigorous definition of when to consider signals to be in a harmonic relationship based on an integer frequency ratio, constant phase, and a well-defined joint instantaneous frequency. We show this definition is linked to extrema counting and Empirical Mode Decomposition (EMD). We explore the mathematics of our definition and link it to results from analytic number theory. This naturally leads to us to define two classes of harmonic structures, termed strong and weak, with different extrema behaviour. We validate our framework using both simulations and real data. Specifically, we look at the harmonics structure in the FitzHugh-Nagumo model and the non-sinusoidal hippocampal theta oscillation in rat local field potential data. We further discuss how our definition helps to address mode splitting in EMD. A clear understanding of when harmonics are present in signals will enable a deeper understanding of the functional and clinical roles of non-sinusoidal neural oscillations.

scholarly journals SPIKY: a graphical user interface for monitoring spike train synchrony

2015 ◽  
Vol 113 (9) ◽  
pp. 3432-3445 ◽  
Author(s):  
Thomas Kreuz ◽  
Mario Mulansky ◽  
Nebojsa Bozanic
Keyword(s):  
User Interface ◽  
Spike Train ◽  
Graphical User Interface ◽  
Large Scale ◽  
Spatial Scales ◽  
Real Data ◽  
Basic Knowledge ◽  
High Temporal Resolution ◽  
Time Resolved ◽  
Significance Levels

Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.

VIX VERSUS VXX: A JOINT ANALYTICAL FRAMEWORK

2020 ◽  
Vol 23 (05) ◽  
pp. 2050033 ◽  
Author(s):  
MARTINO GRASSELLI ◽  
LAKSHITHE WAGALATH
Keyword(s):  
Term Structure ◽  
Real Data ◽  
Analytical Framework ◽  
Option Prices ◽  
Options Market ◽  
Vix Futures ◽  
Consistent Manner ◽  
Systematic Behavior ◽  
Exchange Traded Notes ◽  
Reference Index

We propose a framework for modeling in a consistent manner the VIX index and the VXX, an exchange-traded note written on the VIX. Our study enables to link the properties of VXX to those of the VIX in a tractable way. In particular, we quantify the systematic loss observed empirically for VXX when the VIX futures term-structure is in contango and we derive option prices, implied volatilities and skews of VXX from those of VIX in infinitesimal developments. We also perform a calibration on real data which highlights the flexibility of our model in fitting the futures and the vanilla options market of VIX and VXX. Our framework can be used to model other exchange-traded notes on the VIX as well as any market where exchange-traded notes have been introduced on a reference index, hence providing tools to better anticipate and quantify systematic behavior of an exchange-traded note with respect to the underlying index.

scholarly journals Changing motivations during migration: linking movement speed to reproductive status in a migratory large mammal

2014 ◽  
Vol 10 (6) ◽  
pp. 20140379 ◽  
Author(s):  
Navinder J. Singh ◽  
Göran Ericsson
Keyword(s):  
Animal Ecology ◽  
Animal Movement ◽  
Movement Patterns ◽  
Reproductive Status ◽  
Movement Speed ◽  
Field Observations ◽  
Large Mammal ◽  
Movement Data ◽  
Calf Survival ◽  
Reproductive Females

A challenge in animal ecology is to link animal movement to demography. In general, reproducing and non-reproducing animals may show different movement patterns. Dramatic changes in reproductive status, such as the loss of an offspring during the course of migration, might also affect movement. Studies linking movement speed to reproductive status require individual monitoring of life-history events and hence are rare. Here, we link movement data from 98 GPS-collared female moose ( Alces alces ) to field observations of reproductive status and calf survival. We show that reproductive females move more quickly during migration than non-reproductive females. Further, the loss of a calf over the course of migration triggered a decrease in speed of the female. This is in contrast to what might be expected for females no longer constrained by an accompanying offspring. The observed patterns demonstrate that females of different reproductive status may have distinct movement patterns, and that the underlying motivation to move may be altered by a change in reproductive status during migration.

scholarly journals Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother

2009 ◽  
Vol 184 (2) ◽  
pp. 365-374 ◽  
Author(s):  
David P. Nguyen ◽  
Matthew A. Wilson ◽  
Emery N. Brown ◽  
Riccardo Barbieri
Keyword(s):  
Local Field ◽  
Instantaneous Frequency ◽  
Local Field Potential ◽  
Field Potential ◽  
Potential Oscillations ◽  
Kalman Smoother

scholarly journals Timely poacher detection and localization using sentinel animal movement

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Henrik J. de Knegt ◽  
Jasper A. J. Eikelboom ◽  
Frank van Langevelde ◽  
W. François Spruyt ◽  
Herbert H. T. Prins
Keyword(s):  
High Speed ◽  
Animal Movement ◽  
Predictive Analytics ◽  
Wearable Sensors ◽  
Warning System ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Movement Speed ◽  
Population Declines ◽  
Sentinel Animal

AbstractWildlife crime is one of the most profitable illegal industries worldwide. Current actions to reduce it are far from effective and fail to prevent population declines of many endangered species, pressing the need for innovative anti-poaching solutions. Here, we propose and test a poacher early warning system that is based on the movement responses of non-targeted sentinel animals, which naturally respond to threats by fleeing and changing herd topology. We analyzed human-evasive movement patterns of 135 mammalian savanna herbivores of four different species, using an internet-of-things architecture with wearable sensors, wireless data transmission and machine learning algorithms. We show that the presence of human intruders can be accurately detected (86.1% accuracy) and localized (less than 500 m error in 54.2% of the experimentally staged intrusions) by algorithmically identifying characteristic changes in sentinel movement. These behavioral signatures include, among others, an increase in movement speed, energy expenditure, body acceleration, directional persistence and herd coherence, and a decrease in suitability of selected habitat. The key to successful identification of these signatures lies in identifying systematic deviations from normal behavior under similar conditions, such as season, time of day and habitat. We also show that the indirect costs of predation are not limited to vigilance, but also include (1) long, high-speed flights; (2) energetically costly flight paths; and (3) suboptimal habitat selection during flights. The combination of wireless biologging, predictive analytics and sentinel animal behavior can benefit wildlife conservation via early poacher detection, but also solve challenges related to surveillance, safety and health.

scholarly journals Selective Participation of Single Cortical Neurons in Neuronal Avalanches

2021 ◽  
Vol 14 ◽  
Author(s):  
Timothy Bellay ◽  
Woodrow L. Shew ◽  
Shan Yu ◽  
Jessica J. Falco-Walter ◽  
Dietmar Plenz
Keyword(s):  
Information Processing ◽  
Cortical Neurons ◽  
Field Potential ◽  
High Temporal Resolution ◽  
Single Neurons ◽  
Scale Invariant ◽  
Neuronal Groups ◽  
Neuronal Avalanches

Neuronal avalanches are scale-invariant neuronal population activity patterns in the cortex that emerge in vivo in the awake state and in vitro during balanced excitation and inhibition. Theory and experiments suggest that avalanches indicate a state of cortex that improves numerous aspects of information processing by allowing for the transient and selective formation of local as well as system-wide spanning neuronal groups. If avalanches are indeed involved with information processing, one might expect that single neurons would participate in avalanche patterns selectively. Alternatively, all neurons could participate proportionally to their own activity in each avalanche as would be expected for a population rate code. Distinguishing these hypotheses, however, has been difficult as robust avalanche analysis requires technically challenging measures of their intricate organization in space and time at the population level, while also recording sub- or suprathreshold activity from individual neurons with high temporal resolution. Here, we identify repeated avalanches in the ongoing local field potential (LFP) measured with high-density microelectrode arrays in the cortex of awake nonhuman primates and in acute cortex slices from young and adult rats. We studied extracellular unit firing in vivo and intracellular responses of pyramidal neurons in vitro. We found that single neurons participate selectively in specific LFP-based avalanche patterns. Furthermore, we show in vitro that manipulating the balance of excitation and inhibition abolishes this selectivity. Our results support the view that avalanches represent the selective, scale-invariant formation of neuronal groups in line with the idea of Hebbian cell assemblies underlying cortical information processing.

scholarly journals Rapid Lipolytic Oscillations in Ex-Vivo Adipose Tissue Explants Revealed Through Microfluidic Droplet Sampling at High Temporal Resolution

2019 ◽  
Author(s):  
Juan Hu ◽  
xiangpeng li ◽  
Robert L. Judd ◽  
Christopher Easley
Keyword(s):  
Adipose Tissue ◽  
High Resolution ◽  
Temporal Resolution ◽  
Ex Vivo ◽  
Analytical Framework ◽  
Tissue Level ◽  
Droplet Microfluidics ◽  
Biological Information ◽  
High Temporal Resolution ◽  
Tissue Explants

Our understanding of adipose tissue biology has steadily evolved. While structural and energy storage functionalities have been in the forefront, a key endocrine role for adipocytes was revealed only over the last few decades. In contrast to the wealth of information on dynamic function of other endocrine tissues, few studies have focused on dynamic adipose tissue function or on tool development toward that end. Here, we apply our unique droplet-based microfluidic devices to culture, perfuse, and sample secretions from primary murine epididymal white adipose tissue (eWAT), and from predifferentiated clusters of 3T3-L1 adipocytes. Through automated control, oil-segmented aqueous droplets (~2.6 nL) were sampled from tissue or cells at 3.5-second temporal resolution, with integrated enzyme assays enabling real-time quantification of glycerol (down to 1.9 fmol droplet<sup>-1</sup>). This high resolution revealed previously unreported oscillations in secreted glycerol at frequencies of 0.2 to 2.0 min<sup>-1</sup> (~30-300 s periods) present in the primary tissue but not in clustered cells. Low-level bursts (~50 fmol) released in basal conditions were contrasted with larger bursts (~300 fmol) during stimulation. Further, both fold changes and burst magnitudes were decreased in eWAT of aged and obese mice. These results, combined with immunostaining and photobleaching analyses, suggest that gap-junctional coupling or nerve cell innervation within the intact ex-vivo tissue explants play important roles in this apparent tissue-level, lipolytic synchronization. High-resolution, quantitative sampling by droplet microfluidics thus permitted unique biological information to be observed, giving an analytical framework poised for future studies of dynamic oscillatory function of adipose and other tissues.

scholarly journals Effects of visual inputs on neural dynamics for coding of location and running speed in medial entorhinal cortex

2020 ◽  
Author(s):  
Holger Dannenberg ◽  
Hallie Lazaro ◽  
Pranav Nambiar ◽  
Alec Hoyland ◽  
Michael E. Hasselmo
Keyword(s):  
Entorhinal Cortex ◽  
Field Potential ◽  
Grid Cell ◽  
Spatial Location ◽  
Neural Dynamics ◽  
Movement Speed ◽  
Running Speed ◽  
Medial Entorhinal Cortex ◽  
Visual Inputs ◽  
Cell Firing

ABSTRACTNeuronal representations of spatial location and movement speed in the medial entorhinal cortex during the “active” theta state of the brain are important for memory-guided navigation and rely on visual inputs. However, little is known about how visual inputs change neural dynamics as a function of running speed and time. By manipulating visual inputs in mice, we demonstrate that changes in spatial stability of grid cell firing as a function of time correlate with changes in a proposed speed signal by local field potential theta frequency. In contrast, visual inputs do not affect the speed modulation of firing rates. Moreover, we provide evidence that sensory inputs other than visual inputs can support grid cell firing, though less accurately, in complete darkness. Finally, changes in spatial accuracy of grid cell firing on a 10-s time scale suggest that grid cell firing is a function of velocity signals integrated over past time.

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