Local Shuffling of Spike Trains Boosts the Accuracy of Spike Train Spectral Analysis

2006 ◽  
Vol 95 (5) ◽  
pp. 3245-3256 ◽  
Author(s):  
Michal Rivlin-Etzion ◽  
Ya'acov Ritov ◽  
Gali Heimer ◽  
Hagai Bergman ◽  
Izhar Bar-Gad
Keyword(s):  
Spectral Analysis ◽  
Spike Train ◽  
Discharge Rate ◽  
Low Frequency ◽  
Spike Trains ◽  
High Frequencies ◽  
Refractory Periods ◽  
Low Frequency Oscillations ◽  
Neuronal Spike Trains ◽  
Frequency Modulations

Spectral analysis of neuronal spike trains is an important tool in understanding the characteristics of neuronal activity by providing insights into normal and pathological periodic oscillatory phenomena. However, the refractory period creates high-frequency modulations in spike-train firing rate because any rise in the discharge rate causes a descent in subsequent time bins, leading to multifaceted modifications in the structure of the spectrum. Thus the power spectrum of the spiking activity (autospectrum) displays elevated energy in high frequencies relative to the lower frequencies. The spectral distortion is more dominant in neurons with high firing rates and long refractory periods and can lead to reduced identification of low-frequency oscillations (such as the 5- to 10-Hz burst oscillations typical of Parkinsonian basal ganglia and thalamus). We propose a compensation process that uses shuffling of interspike intervals (ISIs) for reliable identification of oscillations in the entire frequency range. This compensation is further improved by local shuffling, which preserves the slow changes in the discharge rate that may be lost in global shuffling. Cross-spectra of pairs of neurons are similarly distorted regardless of their correlation level. Consequently, identification of low-frequency synchronous oscillations, even for two neurons recorded by a single electrode, is improved by ISI shuffling. The ISI local shuffling is computed with confidence limits that are based on the first-order statistics of the spike trains, thus providing a reliable estimation of auto- and cross-spectra of spike trains and making it an optimal tool for physiological studies of oscillatory neuronal phenomena.

scholarly journals Monitoring the circulation of coastal waters off the Southern coast of Crimea

2019 ◽  
Vol 485 (4) ◽  
pp. 507-510
Author(s):  
V. A. Ivanov ◽  
A. S. Kuznetsov ◽  
A. N. Morozov
Keyword(s):  
Spectral Analysis ◽  
Coastal Zone ◽  
Coastal Waters ◽  
Low Frequency ◽  
Water Dynamics ◽  
Southern Coast ◽  
Jet Streams ◽  
Integrated Monitoring ◽  
Low Frequency Oscillations ◽  
The Crimea

The paper presents the results of integrated monitoring of water dynamics in the coastal zone off the Southern coast of the Crimea for 2008-2016. The regime and features of circulation of coastal waters were determined. An intensive contribution of low-frequency oscillations of coastal currents at the seasonal and synoptic scales was identified based on the results of spectral analysis of a nine-year set of vector dynamics series. For water fluctuations in the coastal zone, the alongshore structure of reciprocating jet streams caused by water dynamics at the shelf and continental slope dominates.

scholarly journals Low-frequency oscillations of the neural drive to the muscle are increased with experimental muscle pain

2012 ◽  
Vol 107 (3) ◽  
pp. 958-965 ◽  
Author(s):  
Dario Farina ◽  
Francesco Negro ◽  
Leonardo Gizzi ◽  
Deborah Falla
Keyword(s):  
Motor Unit ◽  
Hypertonic Saline ◽  
Muscle Pain ◽  
Low Frequency ◽  
Spike Trains ◽  
Isometric Contractions ◽  
Nociceptive Stimulation ◽  
Neural Drive ◽  
Low Frequency Oscillations ◽  
Unit Spike

We investigated the influence of nociceptive stimulation on the accuracy of task execution and motor unit spike trains during low-force isometric contractions. Muscle pain was induced by infusion of hypertonic saline into the abductor digiti minimi muscle of 11 healthy men. Intramuscular EMG signals were recorded from the same muscle during four isometric contractions of 60-s duration at 10% of the maximal force [maximal voluntary contraction (MVC)] performed before injection (baseline), after injection of isotonic (control) or hypertonic saline (pain), and 15 min after pain was no longer reported. Each contraction was preceded by three 3-s ramp contractions from 0% to 10% MVC. The low-frequency oscillations of motor unit spike trains were analyzed by the first principal component of the low-pass filtered spike trains [first common component (FCC)], which represents the effective neural drive to the muscle. Pain decreased the accuracy of task performance [coefficient of variation (CoV) for force: baseline, 2.8 ± 1.8%, pain, 3.9 ± 1.8%; P < 0.05] and reduced motor unit discharge rates [11.6 ± 2.3 pulses per second (pps) vs. 10.7 ± 1.7 pps; P < 0.05]. Motor unit recruitment thresholds (2.2 ± 1.2% MVC vs. 2.4 ± 1.6% MVC), interspike interval variability (18.4 ± 4.9% vs. 19.1 ± 5.4%), strength of motor unit short-term synchronization [common input strength (CIS) 1.02 ± 0.44 vs. 0.83 ± 0.22], and strength of common drive (0.47 ± 0.08 vs. 0.47 ± 0.06) did not change across conditions. The FCC signal was correlated with force ( R = 0.45 ± 0.06), and the CoV for FCC increased in the painful condition (5.69 ± 1.29% vs. 7.83 ± 2.61%; P < 0.05). These results indicate that nociceptive stimulation increased the low-frequency variability in synaptic input to motoneurons.

scholarly journals Joint Probability-Based Neuronal Spike Train Classification

2009 ◽  
Vol 10 (3) ◽  
pp. 229-239 ◽  
Author(s):  
Yan Chen ◽  
Vitaliy Marchenko ◽  
Robert F. Rogers
Keyword(s):  
Spike Train ◽  
Classification Accuracy ◽  
Joint Probability ◽  
Spike Trains ◽  
Observation Time ◽  
Lung Inflation ◽  
Neuronal Spike ◽  
Constant Rate ◽  
Neuronal Spike Trains ◽  
Different Response

Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume) consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response) in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

scholarly journals Modulation of high frequency by low-frequency Oscillations in the basal ganglia

2017 ◽  
Author(s):  
D. Nouri ◽  
R. Ebrahimpour ◽  
A. Mirzaei
Keyword(s):  
Basal Ganglia ◽  
Network Model ◽  
Large Scale ◽  
Low Frequency ◽  
Neural Mechanisms ◽  
Oscillatory Activity ◽  
Beta Band ◽  
Low Frequency Oscillations ◽  
Delta Band ◽  
Frequency Modulations

AbstractModulation of beta band fioscillatory activity (15-30 Hz) by delta band oscillatory activity (1-3 Hz) in the cortico-basal ganglia loop is important for normal basal ganglia functions. However, the neural mechanisms underlying this modulation are poorly understood. To understand the mechanisms underlying such frequency modulations in the basal ganglia, we use large scale subthalamo-pallidal network model stimulated via a delta-frequency input signal. We show that inhibition of external Globus Pallidus (GPe) and excitation of the Subthalamic nucleus (STN) using the delta-band stimulation leads to the same delta-beta interactions in the network model as the experimental results observed in healthy basal ganglia. In addition, we show that pathological beta oscillations in the network model decorrelates the delta-beta link in the network model. In general, using our simulation results, we propose that striato-pallidal inhibition and cortico-subthalamic excitation are the potential sources of the delta-beta link observed in the intact basal ganglia.

Patterns of spontaneous discharge in primate spinothalamic neurons

1989 ◽  
Vol 61 (1) ◽  
pp. 106-115 ◽  
Author(s):  
D. J. Surmeier ◽  
C. N. Honda ◽  
W. D. Willis
Keyword(s):  
Discharge Rate ◽  
Short Interval ◽  
Low Frequency ◽  
Spike Trains ◽  
Spontaneous Discharge ◽  
Spinothalamic Tract ◽  
Data Set ◽  
Lumbosacral Spinal Cord ◽  
Stimulus Response ◽  
Response Classification

1. The spontaneous discharge of 30 spinothalamic tract (STT) neurons in the lumbosacral spinal cord of anesthetized monkeys was studied. Interval, correlation, and spectral analyses were performed. 2. Three patterns of discharge were found; these were referred to as the SP1, SP2, and SP3 patterns. 3. The SP1 group had moderately regular discharge trains that were devoid of short-interval spike bursts. 4. The SP2 group had spike trains dominated by short-interval bursts without evidence of low-frequency rhythmicity. 5. The SP3 group had spike trains with features of both the SP1 and SP2 groups. 6. Some correlations were found between the mean discharge rate and stimulus-response classes previously defined by our group (25). Correlations were deduced from a parent data set of 221 STT neurons. Type 1 neurons, which were driven primarily by tactile afferents, were found to have significantly lower mean rates than other "within-neuron" groups. On the other hand, type C neurons, which had strong input from afferents signalling pressure and noxious stimuli, were found to have significantly higher mean rates than all other "across-neuron" classes. 7. A weak relationship was found between the pattern of discharge and the within-neuron stimulus-response classification. Neurons with a largely tactile coding orientation (types 1 and 2) were most frequently of the SP1 and SP2 classes, whereas neurons with a more prominent nociceptive input (types 3 and 4) were most frequently of the SP2 and SP3 classes. No relationship was apparent between discharge pattern and the across-neuron classes.

scholarly journals Monitoring spike train synchrony

2013 ◽  
Vol 109 (5) ◽  
pp. 1457-1472 ◽  
Author(s):  
Thomas Kreuz ◽  
Daniel Chicharro ◽  
Conor Houghton ◽  
Ralph G. Andrzejak ◽  
Florian Mormann
Keyword(s):  
Spike Train ◽  
Real Time ◽  
Spike Trains ◽  
Substantial Improvement ◽  
Time Resolved ◽  
Original Definition ◽  
Temporal Averaging ◽  
Instantaneous Values ◽  
Eeg Recordings ◽  
Neuronal Spike Trains

Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.

The Dynamics of Integrate-and-Fire: Mean Versus Variance Modulations and Dependence on Baseline Parameters

2011 ◽  
Vol 23 (5) ◽  
pp. 1234-1247 ◽  
Author(s):  
Joanna Pressley ◽  
Todd W. Troyer
Keyword(s):  
Firing Rate ◽  
Cutoff Frequency ◽  
Low Frequency ◽  
High Frequencies ◽  
Integrate And Fire ◽  
Poisson Input ◽  
Low Pass ◽  
Mean And Variance ◽  
The Mean ◽  
Frequency Modulations

The leaky integrate-and-fire (LIF) is the simplest neuron model that captures the essential properties of neuronal signaling. Yet common intuitions are inadequate to explain basic properties of LIF responses to sinusoidal modulations of the input. Here we examine responses to low - and moderate-frequency modulations of both the mean and variance of the input current and quantify how these responses depend on baseline parameters. Across parameters, responses to modulations in the mean current are low pass, approaching zero in the limit of high frequencies. For very low baseline firing rates, the response cutoff frequency matches that expected from membrane integration. However, the cutoff shows a rapid, supralinear increase with firing rate, with a steeper increase in the case of lower noise. For modulations of the input variance, the gain at high frequency remains finite. Here, we show that the low-frequency responses depend strongly on baseline parameters and derive an analytic condition specifying the parameters at which responses switch from being dominated by low versus high frequencies. Additionally, we show that the resonant responses for variance modulations have properties not expected for common oscillatory resonances: they peak at frequencies higher than the baseline firing rate and persist when oscillatory spiking is disrupted by high noise. Finally, the responses to mean and variance modulations are shown to have a complementary dependence on baseline parameters at higher frequencies, resulting in responses to modulations of Poisson input rates that are independent of baseline input statistics.

scholarly journals Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

2012 ◽  
Vol 107 (12) ◽  
pp. 3357-3369 ◽  
Author(s):  
Jakob L. Dideriksen ◽  
Francesco Negro ◽  
Roger M. Enoka ◽  
Dario Farina
Keyword(s):  
Motor Unit ◽  
Low Frequency ◽  
Spike Trains ◽  
Motor Unit Recruitment ◽  
Force Variability ◽  
Muscle Properties ◽  
Single Motor Unit ◽  
Force Steadiness ◽  
Low Frequency Oscillations ◽  
Synaptic Noise

Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (<10% of maximal force) for typical values of these parameters. Results indicate that motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates.

scholarly journals Fano Factor: A Potentially Useful Information

2020 ◽  
Vol 14 ◽  
Author(s):  
Kamil Rajdl ◽  
Petr Lansky ◽  
Lubomir Kostal
Keyword(s):  
Experimental Data ◽  
Spike Train ◽  
Time Window ◽  
Fano Factor ◽  
Spike Trains ◽  
Renewal Processes ◽  
Neuronal Spike ◽  
Markov Renewal Processes ◽  
Neuronal Spike Trains ◽  
Operational Time

The Fano factor, defined as the variance-to-mean ratio of spike counts in a time window, is often used to measure the variability of neuronal spike trains. However, despite its transparent definition, careless use of the Fano factor can easily lead to distorted or even wrong results. One of the problems is the unclear dependence of the Fano factor on the spiking rate, which is often neglected or handled insufficiently. In this paper we aim to explore this problem in more detail and to study the possible solution, which is to evaluate the Fano factor in the operational time. We use equilibrium renewal and Markov renewal processes as spike train models to describe the method in detail, and we provide an illustration on experimental data.

Sign in / Sign up

Export Citation Format

Share Document