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Ciba, Manuel, Robert Bestel, Christoph Nick, Guilherme Ferraz de Arruda, Thomas Peron, Comin César Henrique, Luciano da Fontoura Costa, Francisco Aparecido Rodrigues et Christiane Thielemann. « Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity ». Neural Computation 32, no 5 (mai 2020) : 887–911. http://dx.doi.org/10.1162/neco_a_01277.
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As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains.
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Kreuz, Thomas, Daniel Chicharro, Conor Houghton, Ralph G. Andrzejak et Florian Mormann. « Monitoring spike train synchrony ». Journal of Neurophysiology 109, no 5 (1 mars 2013) : 1457–72. http://dx.doi.org/10.1152/jn.00873.2012.
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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.
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Kreuz, Thomas, Julie S. Haas, Alice Morelli, Henry D. I. Abarbanel et Antonio Politi. « Measuring spike train synchrony ». Journal of Neuroscience Methods 165, no 1 (septembre 2007) : 151–61. http://dx.doi.org/10.1016/j.jneumeth.2007.05.031.
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Kreuz, Thomas, Daniel Chicharro, Ralph G. Andrzejak, Julie S. Haas et Henry D. I. Abarbanel. « Measuring multiple spike train synchrony ». Journal of Neuroscience Methods 183, no 2 (octobre 2009) : 287–99. http://dx.doi.org/10.1016/j.jneumeth.2009.06.039.
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Kreuz, Thomas. « Measures of spike train synchrony ». Scholarpedia 6, no 10 (2011) : 11934. http://dx.doi.org/10.4249/scholarpedia.11934.
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Brody, Carlos D. « Correlations Without Synchrony ». Neural Computation 11, no 7 (1 octobre 1999) : 1537–51. http://dx.doi.org/10.1162/089976699300016133.
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Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out.
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Kreuz, Thomas, Mario Mulansky et Nebojsa Bozanic. « SPIKY : a graphical user interface for monitoring spike train synchrony ». Journal of Neurophysiology 113, no 9 (mai 2015) : 3432–45. http://dx.doi.org/10.1152/jn.00848.2014.
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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.
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Houghton, Conor. « Population measures of spike train synchrony ». Scholarpedia 8, no 10 (2013) : 30635. http://dx.doi.org/10.4249/scholarpedia.30635.
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Kajikawa, Yoshinao, et Troy A. Hackett. « Entropy analysis of neuronal spike train synchrony ». Journal of Neuroscience Methods 149, no 1 (novembre 2005) : 90–93. http://dx.doi.org/10.1016/j.jneumeth.2005.05.011.
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Chen, Zhe. « An Overview of Bayesian Methods for Neural Spike Train Analysis ». Computational Intelligence and Neuroscience 2013 (2013) : 1–17. http://dx.doi.org/10.1155/2013/251905.
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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.
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Tam, David C. « A spike train analysis for quantifying inhibitory near synchrony in spike firings ». Neurocomputing 44-46 (juin 2002) : 1149–53. http://dx.doi.org/10.1016/s0925-2312(02)00441-1.
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Mulansky, Mario, et Thomas Kreuz. « PySpike—A Python library for analyzing spike train synchrony ». SoftwareX 5 (2016) : 183–89. http://dx.doi.org/10.1016/j.softx.2016.07.006.
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Kelly, Ryan C., et Robert E. Kass. « A Framework for Evaluating Pairwise and Multiway Synchrony Among Stimulus-Driven Neurons ». Neural Computation 24, no 8 (août 2012) : 2007–32. http://dx.doi.org/10.1162/neco_a_00307.
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Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point-process regression models of individual neuron activity with log-linear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then assess the amount of data needed to reliably detect multiway spiking.
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Ciba, Manuel, Takuya Isomura, Yasuhiko Jimbo, Andreas Bahmer et Christiane Thielemann. « Spike-contrast : A novel time scale independent and multivariate measure of spike train synchrony ». Journal of Neuroscience Methods 293 (janvier 2018) : 136–43. http://dx.doi.org/10.1016/j.jneumeth.2017.09.008.
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Kreuz, Thomas, Daniel Chicharro, Martin Greschner et Ralph G. Andrzejak. « Time-resolved and time-scale adaptive measures of spike train synchrony ». Journal of Neuroscience Methods 195, no 1 (janvier 2011) : 92–106. http://dx.doi.org/10.1016/j.jneumeth.2010.11.020.
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Satuvuori, Eero, Mario Mulansky, Nebojsa Bozanic, Irene Malvestio, Fleur Zeldenrust, Kerstin Lenk et Thomas Kreuz. « Measures of spike train synchrony for data with multiple time scales ». Journal of Neuroscience Methods 287 (août 2017) : 25–38. http://dx.doi.org/10.1016/j.jneumeth.2017.05.028.
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Lindsey, B. G., Y. M. Hernandez, K. F. Morris, R. Shannon et G. L. Gerstein. « Dynamic reconfiguration of brain stem neural assemblies : respiratory phase-dependent synchrony versus modulation of firing rates ». Journal of Neurophysiology 67, no 4 (1 avril 1992) : 923–30. http://dx.doi.org/10.1152/jn.1992.67.4.923.
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1. The objective of this work was to determine whether configurations of midline brain stem neural assemblies change during the respiratory cycle. 2. Spike trains of several single neurons were recorded simultaneously in anesthetized, paralyzed, bilaterally vagotomized, artificially ventilated cats. Data were analyzed with cross-correlational and gravity methods. 3. Sequential samples from each of eight groups of neurons known to contain synchronously discharging neurons exhibited temporal variations in that synchrony. 4. Gravity analysis of short (less than 200-s) samples of spike train data revealed 20 pairs of clustered particles that were not predicted from cross-correlation analysis of the parent data sets (greater than 20 min). 5. Twenty-nine groups of three to eight simultaneously monitored neurons, each with at least two synchronously discharging neurons, were analyzed for evidence of respiratory phase-dependent modulation of that coordinated activity. Spikes from successive interleaved inspiratory and expiratory intervals were analyzed separately. 6. Neurons pairs in 11 groups were more synchronous during the inspiratory interval; six groups had pairs that were more synchronous during the expiratory period. In two groups, different pairs were synchronous in different respiratory phases. In 11 of the 26 pairs that exhibited phase-dependent differences in synchrony, neither neuron had a respiratory-modulated firing rate as judged by either the cycle-triggered histogram or an analysis of variance of their firing rates. 7. Configurations of respiratory-related brain stem neural networks changed with time and the phases of breathing. Neurons with no apparent respiratory modulation of their individual firing rates collectively exhibited respiratory phase-dependent modulation of their impulse synchrony.(ABSTRACT TRUNCATED AT 250 WORDS)
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Zador, Anthony. « Impact of Synaptic Unreliability on the Information Transmitted by Spiking Neurons ». Journal of Neurophysiology 79, no 3 (1 mars 1998) : 1219–29. http://dx.doi.org/10.1152/jn.1998.79.3.1219.
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Zador, Anthony. Impact of synaptic unreliability on the information transmitted by spiking neurons. J. Neurophysiol. 79: 1219–1229, 1998. The spike generating mechanism of cortical neurons is highly reliable, able to produce spikes with a precision of a few milliseconds or less. The excitatory synapses driving these neurons are by contrast much less reliable, subject both to release failures and quantal fluctuations. This suggests that synapses represent the primary bottleneck limiting the faithful transmission of information through cortical circuitry. How does the capacity of a neuron to convey information depend on the properties of its synaptic drive? We address this question rigorously in an information theoretic framework. We consider a model in which a population of independent unreliable synapses provides the drive to an integrate-and-fire neuron. Within this model, the mutual information between the synaptic drive and the resulting output spike train can be computed exactly from distributions that depend only on a single variable, the interspike interval. The reduction of the calculation to dependence on only a single variable greatly reduces the amount of data required to obtain reliable information estimates. We consider two factors that govern the rate of information transfer: the synaptic reliability and the number of synapses connecting each presynaptic axon to its postsynaptic target (i.e., the connection redundancy, which constitutes a special form of input synchrony). The information rate is a smooth function of both mechanisms; no sharp transition is observed from an “unreliable” to a “reliable” mode. Increased connection redundancy can compensate for synaptic unreliability, but only under the assumption that the fine temporal structure of individual spikes carries information. If only the number of spikes in some relatively long-time window carries information (a “mean rate” code), an increase in the fidelity of synaptic transmission results in a seemingly paradoxical decrease in the information available in the spike train. This suggests that the fine temporal structure of spike trains can be used to maintain reliable transmission with unreliable synapses.
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Steriade, M., et F. Amzica. « Dynamic coupling among neocortical neurons during evoked and spontaneous spike-wave seizure activity ». Journal of Neurophysiology 72, no 5 (1 novembre 1994) : 2051–69. http://dx.doi.org/10.1152/jn.1994.72.5.2051.
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1. We investigated the development from patterns of electroencephalogram (EEG) synchronization to paroxysms consisting of spike-wave (SW) complexes at 2–4 Hz or to seizures at higher frequencies (7–15 Hz). We used multisite, simultaneous EEG, extracellular, and intracellular recordings from various neocortical areas and thalamic nuclei of anesthetized cats. 2. The seizures were observed in 25% of experimental animals, all maintained under ketamine and xylazine anesthesia, and were either induced by thalamocortical volleys and photic stimulation or occurred spontaneously. Out of unit and field potential recordings within 370 cortical and 65 thalamic sites, paroxysmal events occurred in 70 cortical and 8 thalamic sites (approximately 18% and 12%, respectively), within which a total of 181 neurons (143 extracellular and 38 intracellular) were simultaneously recorded in various combinations of cell groups. 3. Stimulus-elicited and spontaneous SW seizures at 2–4 Hz lasted for 15–35 s and consisted of barrages of action potentials related to the spiky depth-negative (surface-positive) field potentials, followed by neuronal silence during the depth-positive wave component of SW complexes. The duration of inhibitory periods progressively increased during the seizure, at the expense of the phasic excitatory phases. 4. Intracellular recordings showed that, during such paroxysms, cortical neurons displayed a tonic depolarization (approximately 10–20 mV), sculptured by rhythmic hyperpolarizations. 5. In all cases, measures of synchrony demonstrated time lags between discharges of simultaneously recorded cortical neurons, from as short as 3–10 ms up to 50 ms or even longer intervals. Synchrony was assessed by cross-correlograms, by a method termed first-spike-analysis designed to detect dynamic temporal relations between neurons and relying on the detection of the first action potential in a spike train, and by a method termed sequential-field-correlation that analyzed the time course of field potentials simultaneously recorded from different cortical areas. 6. The degree of synchrony progressively increased from preseizure sleep patterns to the early stage of the SW seizure and, further, to its late stage. In some cases the time relation between neurons during the early stages of seizures was inversed during late stages. 7. These data show that, although the common definition of SW seizures, regarded as suddenly generalized and bilaterally synchronous activities, may be valid at the macroscopic EEG level, cortical neurons display time lags between their rhythmic spike trains, progressively increased synchrony, and changes in the temporal relations between their discharges during the paroxysms.(ABSTRACT TRUNCATED AT 400 WORDS)
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Shahbaba, Babak, Bo Zhou, Shiwei Lan, Hernando Ombao, David Moorman et Sam Behseta. « A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons ». Neural Computation 26, no 9 (septembre 2014) : 2025–51. http://dx.doi.org/10.1162/neco_a_00631.
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We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows. The nonparametric component (i.e., the gaussian process model) provides a flexible framework for modeling the underlying firing rates, and the parametric component (i.e., the copula model) allows us to make inferences regarding both contemporaneous and lagged relationships among neurons. Using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of a dependence structure among variables. Our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high-dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas.
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Eggermont, J. J., et G. M. Smith. « Synchrony between single-unit activity and local field potentials in relation to periodicity coding in primary auditory cortex ». Journal of Neurophysiology 73, no 1 (1 janvier 1995) : 227–45. http://dx.doi.org/10.1152/jn.1995.73.1.227.
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1. We recorded responses from 136 single units and the corresponding local field potentials (LFPs) from the same electrode at 44 positions in the primary auditory cortex of 25 juvenile, ketamine-anesthetized cats in response to periodic click trains with click repetition rates between 1 and 32 Hz; to Poisson-distributed click trains with an average click rate of 4 Hz; and under spontaneous conditions. The aim of the study is to evaluate the synchrony between LFPs and single-unit responses, to compare their coding of periodic stimuli, and to elucidate mechanisms that limit this periodicity coding in primary auditory cortex. 2. We obtained averaged LFPs either as click-triggered averages, the classical evoked potentials, or as spike-triggered averages. We quantified LFPs by initial negative peak-to-positive peak amplitude. In addition, we obtained trigger events from negativegoing level crossings (at approximately 2 SD below the mean) of the 100-Hz low-pass electrode signal. We analyzed these LFP triggers similarly to single-unit spikes. 3. The average ratio of the LFP amplitude in response to the second click in a train and the LFP amplitude to the first click as a function of click rate was low-pass with a slight resonance at approximately 10 Hz, and, above that frequency, decreasing with a slope of approximately 24 dB/octave. We found the 50% point at approximately 16 Hz. In contrast, the LFP amplitude averaged over entire click trains was low-pass with a similar resonance but a high-frequency slope of 12 dB/octave and a 50% point at approximately 12 Hz. 4. The LFP amplitude for click repetition rates between 5 and 11 Hz often showed augmentation, i.e., the amplitude increased in response to the first few clicks in the train and thereafter decreased. This augmentation was paralleled by an increase in the probability of firing in single units simultaneously recorded on the same electrode. 5. We calculated temporal modulation transfer functions (tMTFs) for single-unit spikes and for LFP triggers. They were typically bandpass with a best modulating frequency of 10 Hz and similar shape for both single-unit spikes and LFP triggers. The tMTF per click, obtained by dividing the tMTF by the number of clicks in the train, was low-pass with a 50% cutoff frequency at approximately Hz, similar to that for the average LFP amplitude. 6. the close similarity of the tMTFs for single-unit spikes and LFP triggers suggests that single-unit tMTFs can be predicted from LFP level crossings.(ABSTRACT TRUNCATED AT 400 WORDS)
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Tam, David C. « A multi-unit spike train analysis for quantifying phase relationships of near-synchrony firings ». Neurocomputing 38-40 (juin 2001) : 945–49. http://dx.doi.org/10.1016/s0925-2312(01)00404-0.
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Swadlow, Harvey A., Irina N. Beloozerova et Mikhail G. Sirota. « Sharp, Local Synchrony Among Putative Feed-Forward Inhibitory Interneurons of Rabbit Somatosensory Cortex ». Journal of Neurophysiology 79, no 2 (1 février 1998) : 567–82. http://dx.doi.org/10.1152/jn.1998.79.2.567.
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Swadlow, Harvey A., Irina N. Beloozerova, and Mikhail G. Sirota. Sharp, local synchrony among putative feed-forward inhibitory interneurons of rabbit somatosensory cortex. J. Neurophysiol. 79: 567–582, 1998. Many suspected inhibitory interneurons (SINs) of primary somatosensory cortex (S1) receive a potent monosynaptic thalamic input (thalamocortical SINs, SINstc). It has been proposed that nearly all such SINstc of a S1 barrel column (BC) receive excitatory synaptic input from each member of a subpopulation of neurons within the topographically aligned ventrobasal (VB) thalamic barreloid. Such a divergent and convergent network leads to several testable predictions: sharply synchronous activity should occur between SINstc of a BC, sharp synchrony should not occur between SINstc of neighboring BCs, and sharp synchrony should not occur between SINs or other neurons of the same BC that do not receive potent monosynaptic thalamic input. These predictions were tested by cross-correlating the activity of SINstc of the same and neighboring BCs. Correlations among descending corticofugal neurons of layer 5 (CF-5 neurons, identified by antidromic activation) and other neurons that receive little or no monosynaptic VB input also were examined. SINs were identified by a high-frequency (>600 Hz) burst of three or more spikes elicited by VB stimulation and had action potentials of short duration. SINstc were further differentiated by short synaptic latencies to electrical stimulation of VB thalamus (<1.7 ms) and to peripheral stimulation (<7.5 ms). The above predictions were confirmed fully. 1) Sharp synchrony (±1 ms) was seen between all SINstc recorded within the same BC (a mean of 4.26% of the spikes of each SINtc were synchronized sharply with the spikes of the paired SINtc). Sharp synchrony was not dependent on peripheral stimulation, was not oscillatory, and survived general anesthesia. Sharp synchrony was superimposed on a broader synchrony, with a time course of tens of milliseconds. 2) Little or no sharp synchrony was seen when CF-5 neurons were paired with SINstc or other neurons of the same BC. 3) Little or no sharp synchrony was seen when SINstc were paired with other SINstc located in neighboring BCs. Intracellular recordings obtained from three SINs in the fully awake state supported the assertion that SINs are GABAergic interneurons. Each of these cells met our extracellular criteria for identification as a SIN, each had a spike of short duration (0.4–0.5 ms), and each responded to a depolarizing current pulse with a nonadapting train of action potentials. These results support the proposed network linking VB barreloid neurons with SINstc within the topographically aligned BC. We suggest that sharp synchrony among SINstc results in highly synchronous inhibitory postsynpatic potentials (IPSPs)in the target neurons of these cells and that these summated IPSPs may be especially effective when excitatory drive to target cells is weak and asynchronous.
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Ventura, Valérie, Can Cai et Robert E. Kass. « Trial-to-Trial Variability and Its Effect on Time-Varying Dependency Between Two Neurons ». Journal of Neurophysiology 94, no 4 (octobre 2005) : 2928–39. http://dx.doi.org/10.1152/jn.00644.2004.
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The joint peristimulus time histogram (JPSTH) and cross-correlogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper we showed how a Bootstrap evaluation of the peaks in the smoothed diagonals of the JPSTH may be used to establish the likely validity of apparent time-varying correlation. As noted in earlier studies by Brody and Ben-Shaul et al., trial-to-trial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a method of estimating the time-dependent trial-to-trial variation in spike trains that may exceed the natural variation displayed by Poisson and non-Poisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firing-rate function that fluctuates over time. The method developed here decomposes the spike-train variability into a stimulus-related component and a trial-specific component, allowing many degrees of freedom to characterize the former while assuming a small number suffices to characterize the latter. The Bootstrap significance test of the companion paper is then modified to accommodate these general excitability effects. This methodology allows an investigator to assess whether excitability effects are constant or time-varying, and whether they are shared by two neurons. In data from two V1 neurons we find that highly statistically significant evidence of dependency disappears after adjustment for time-varying trial-to-trial variation.
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Khanbabaie, Reza, William H. Nesse, Andre Longtin et Leonard Maler. « Kinetics of Fast Short-Term Depression Are Matched to Spike Train Statistics to Reduce Noise ». Journal of Neurophysiology 103, no 6 (juin 2010) : 3337–48. http://dx.doi.org/10.1152/jn.00117.2010.
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Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (∼5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.
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Lindsey, B. G., L. S. Segers, K. F. Morris, Y. M. Hernandez, S. Saporta et R. Shannon. « Distributed actions and dynamic associations in respiratory-related neuronal assemblies of the ventrolateral medulla and brain stem midline : evidence from spike train analysis ». Journal of Neurophysiology 72, no 4 (1 octobre 1994) : 1830–51. http://dx.doi.org/10.1152/jn.1994.72.4.1830.
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1. Considerable evidence indicates that neurons in the brain stem midline and ventrolateral medulla participate in the control of breathing. This work was undertaken to detect and evaluate evidence for functional links that coordinate the parallel operations of neurons distributed in these two domains. 2. Data were from 51 Dial-urethan-anesthetized, bilaterally vagotomized, paralyzed, artificially ventilated cats. Planar arrays of tungsten microelectrodes were used to monitor simultaneously spike trains in two or three of the following regions: n. raphe obscurus-n. raphe pallidus, n. raphe magnus, rostral ventrolateral medulla, and caudal ventrolateral medulla. Efferent phrenic nerve activity was recorded to indicate the phases of the respiratory cycle. Electrodes in the ventral spinal cord (C3) were used in antidromic stimulation tests for spinal projections of neurons. 3. Spike trains of 1,243 neurons were tested for respiratory modulated firing rates with cycle-triggered histograms and an analysis of variance with the use of a subjects-by-treatments experimental design. Functional associations were detected and evaluated with cross-correlograms, snowflakes, and the gravity method. 4. Each of 2,310 pairs of neurons studied included one neuron monitored within 0.6 nm of the brain stem midline and a second cell recorded in the ventrolateral medulla; 117 of these pairs (5%) included a neuron with a spinal projection, identified with antidromic stimulation methods, that extended to at least the third cervical segment. Short-time scale correlations were detected in 110 (4.7%) pairs of neurons. Primary cross-correlogram features included 40 central peaks, 47 offset peaks, 4 central troughs, and 19 offset troughs. 5. In 14 data sets, multiple short-time scale correlations were found among three or more simultaneously recorded neurons distributed between both midline and ventrolateral domains. The results suggested that elements of up to three layers of interneurons were monitored simultaneously. Evidence for concurrent serial and parallel regulation of impulse synchrony was detected. Gravitational representations demonstrated respiratory-phase dependent synchrony among neurons distributed in both brain stem regions. 6. The results support a model of the brain stem respiratory network composed of coordinated distributed subassemblies and provide evidence for several hypotheses. 1) Copies of respiratory drive information from rostral ventrolateral medullary (RVLM) respiratory neurons are transmitted to midline neurons. 2) Midline neurons act on respiratory-related neurons in the RVLM to modulate phase timing. 3) Impulse synchrony of midline neurons is influenced by concurrent divergent actions of both midline and ventrolateral neurons.(ABSTRACT TRUNCATED AT 400 WORDS)
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Cui, Jianxia, Carmen C. Canavier et Robert J. Butera. « Functional Phase Response Curves : A Method for Understanding Synchronization of Adapting Neurons ». Journal of Neurophysiology 102, no 1 (juillet 2009) : 387–98. http://dx.doi.org/10.1152/jn.00037.2009.
Texte intégralRésumé :
Phase response curves (PRCs) for a single neuron are often used to predict the synchrony of mutually coupled neurons. Previous theoretical work on pulse-coupled oscillators used single-pulse perturbations. We propose an alternate method in which functional PRCs (fPRCs) are generated using a train of pulses applied at a fixed delay after each spike, with the PRC measured when the phasic relationship between the stimulus and the subsequent spike in the neuron has converged. The essential information is the dependence of the recovery time from pulse onset until the next spike as a function of the delay between the previous spike and the onset of the applied pulse. Experimental fPRCs in Aplysia pacemaker neurons were different from single-pulse PRCs, principally due to adaptation. In the biological neuron, convergence to the fully adapted recovery interval was slower at some phases than that at others because the change in the effective intrinsic period due to adaptation changes the effective phase resetting in a way that opposes and slows the effects of adaptation. The fPRCs for two isolated adapting model neurons were used to predict the existence and stability of 1:1 phase-locked network activity when the two neurons were coupled. A stability criterion was derived by linearizing a coupled map based on the fPRC and the existence and stability criteria were successfully tested in two-simulated-neuron networks with reciprocal inhibition or excitation. The fPRC is the first PRC-based tool that can account for adaptation in analyzing networks of neural oscillators.
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Takanen, Marko, et Bernhard U. Seeber. « A Phenomenological Model Reproducing Temporal Response Characteristics of an Electrically Stimulated Auditory Nerve Fiber ». Trends in Hearing 26 (janvier 2022) : 233121652211170. http://dx.doi.org/10.1177/23312165221117079.
Texte intégralRésumé :
The ability of cochlear implants (CIs) to restore hearing to profoundly deaf people is based on direct electrical stimulation of the auditory nerve fibers (ANFs). Still, CI users do not achieve as good hearing outcomes as their normal-hearing peers. The development and optimization of CI stimulation strategies to reduce that gap could benefit from computational models that can predict responses evoked by different stimulation patterns, particularly temporal responses for coding of temporal fine structure information. To that end, we present the sequential biphasic leaky integrate-and-fire (S-BLIF) model for the ANF response to various pulse shapes and temporal sequences. The phenomenological S-BLIF model is adapted from the earlier BLIF model that can reproduce neurophysiological single-fiber cat ANF data from single-pulse stimulations. It was extended with elements that simulate refractoriness, facilitation, accommodation and long-term adaptation by affecting the threshold value of the model momentarily after supra- and subthreshold stimulation. Evaluation of the model demonstrated that it can reproduce neurophysiological data from single neuron recordings involving temporal phenomena related to inter-pulse interactions. Specifically, data for refractoriness, facilitation, accommodation and spike-rate adaptation can be reproduced. In addition, the model can account for effects of pulse rate on the synchrony between the pulsatile input and the spike-train output. Consequently, the model offers a versatile tool for testing new coding strategies for, e.g., temporal fine structure using pseudo-monophasic pulses, and for estimating the status of the electrode-neuron interface in the CI user's cochlea.
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Tiesinga, Paul H. E. « Stimulus Competition by Inhibitory Interference ». Neural Computation 17, no 11 (1 novembre 2005) : 2421–53. http://dx.doi.org/10.1162/0899766054796905.
Texte intégralRésumé :
When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli when presented alone (Reynolds, Chelazzi, & Desimone, 1999). When attention is directed toward the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases slightly, but the coherence between the neuron's spike train and the local field potential can increase (Fries, Reynolds, Rorie, & Desimone, 2001). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by the activity of the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays, it approached the firing rate of the poor stimulus. When either stimulus was presented alone, the neuron's response was not altered by the change in delay, but could change due to modulation of the degree of synchrony of the corresponding interneuron network. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons primarily by changing the relative timing of inhibition, whereas changes in the degree of synchrony of interneuron networks modulate the response to a single stimulus. The new mechanism proposed here for attentional modulation of firing rate, gain modulation by inhibitory interference, is likely to have more general applicability to cortical information processing.
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Guyonneau, Rudy, Rufin VanRullen et Simon J. Thorpe. « Neurons Tune to the Earliest Spikes Through STDP ». Neural Computation 17, no 4 (1 avril 2005) : 859–79. http://dx.doi.org/10.1162/0899766053429390.
Texte intégralRésumé :
Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.
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Picado Muiño, David, Iván Castro León et Christian Borgelt. « Fuzzy characterization of spike synchrony in parallel spike trains ». Soft Computing 18, no 1 (7 avril 2013) : 71–83. http://dx.doi.org/10.1007/s00500-013-1034-6.
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Senn, W., I. Segev et M. Tsodyks. « Reading Neuronal Synchrony with Depressing Synapses ». Neural Computation 10, no 4 (1 mai 1998) : 815–19. http://dx.doi.org/10.1162/089976698300017494.
Texte intégralRésumé :
A recent experiment showed that neurons in the primary auditory cortex of the monkey do not change their mean firing rate during an ongoing tone stimulus. The only change was an enhanced correlation among the individual spike trains during the tone. We show that there is an easy way to extract this coherence information in the cortical cell population by projecting the spike trains through depressing synapses onto a postsynaptic neuron.
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Lepage, Kyle Q., Mark A. Kramer et Uri T. Eden. « The Dependence of Spike Field Coherence on Expected Intensity ». Neural Computation 23, no 9 (septembre 2011) : 2209–41. http://dx.doi.org/10.1162/neco_a_00169.
Texte intégralRésumé :
The coherence between neural spike trains and local-field potential recordings, called spike-field coherence, is of key importance in many neuroscience studies. In this work, aside from questions of estimator performance, we demonstrate that theoretical spike-field coherence for a broad class of spiking models depends on the expected rate of spiking. This rate dependence confounds the phase locking of spike events to field-potential oscillations with overall neuron activity and is demonstrated analytically, for a large class of stochastic models, and in simulation. Finally, the relationship between the spike-field coherence and the intensity field coherence is detailed analytically. This latter quantity is independent of neuron firing rate and, under commonly found conditions, is proportional to the probability that a neuron spikes at a specific phase of field oscillation. Hence, intensity field coherence is a rate-independent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony.
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Golomb, David. « Models of Neuronal Transient Synchrony During Propagation of Activity Through Neocortical Circuitry ». Journal of Neurophysiology 79, no 1 (1 janvier 1998) : 1–12. http://dx.doi.org/10.1152/jn.1998.79.1.1.
Texte intégralRésumé :
Golomb, David. Models of neuronal transient synchrony during propagation of activity through neorcortical circuitry. J. Neurophysiol. 79: 1–12, 1998. Stereotypic paroxysmal discharges that propagate in neocortical tissues after electrical stimulations are used as a probe for studying cortical circuitry. I use modeling to investigate the effects of sparse connectivity, heterogeneity of intrinsic neuronal properties, and synaptic noise on synchronization of evoked propagating neuronal discharges in a network of excitatory, regular spiking neurons with spatially decaying connectivity. The global coherence of the traveling discharge is characterized by the correlation function between spike trains of neurons, averaged over all the pairs of neurons in the system at the same distance. Local coherence of two neurons is characterized by their correlation function averaged over many trials or, for persistent activity, over a long time interval. Spike synchronization between neurons emerges as a result of the transient activity; if activity is persistent, there is no synchrony, and cross-correlation functions are flat. During discharge propagation, system-average cross-correlation between neurons does not depend on their mutual distance except for a time shift. Spike synchronization occurs only when the average number of synapses M a cell receives is large enough. As M increases, there is a cross-over from an asynchronized to a synchronized discharge. Synaptic depression appears to help synchrony; it reduces the M value at the cross-over. The strengths of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartate (NMDA) conductances affect synchrony only weakly. Spike synchronization is robust even with large levels of heterogeneity. Synaptic noise reduces synchrony, but strong synchrony is observed at a noise level that cannot evoke spontaneous discharges. System-average spike synchronization is determined by the levels of sparseness, heterogeneity, and noise, whereas trial-average spike synchronization is determined only by the noise level. Therefore, I predict that experiments will reveal local, two-cell spike synchrony, but not global synchrony.
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Lindsey, B. G., K. F. Morris, R. Shannon et G. L. Gerstein. « Repeated Patterns of Distributed Synchrony in Neuronal Assemblies ». Journal of Neurophysiology 78, no 3 (1 septembre 1997) : 1714–19. http://dx.doi.org/10.1152/jn.1997.78.3.1714.
Texte intégralRésumé :
Lindsey, B. G., K. F. Morris, R. Shannon, and G. L. Gerstein. Repeated patterns of distributed synchrony in neuronal assemblies. J. Neurophysiol. 78: 1714–1719, 1997. Models of brain function predict that the recurrence of a process or state will be reflected in repeated patterns of correlated activity. Previous work on medullary raphe assembly dynamics revealed transient changes inimpulse synchrony. This study tested the hypothesis that these variations in synchrony include distributed nonrandom patterns of association. Spike trains were recorded simultaneously in the ventrolateral medulla, n. raphe obscurus, and n. raphe magnus of four anesthetized (Dial), vagotomized, paralyzed, and artificially ventilated adult cats. The “gravitational” representation of spike trains was used to detect moments of impulse synchrony in neuronal assemblies visualized as variations in the aggregation velocities of particles corresponding to each neuron. Template matching algorithms were developed to identify excessively repeating patterns of particle condensation rates. Repeating patterns weredetected in each animal. The reiterated patterns represented anemergent property not apparent in either corresponding firing rate histograms or conventional gravity representations. Overlapping subsets of neurons represented in different patterns were unmasked when the template resolution was changed. The results demonstrate repeated transient network configurations defined by the tightness and duration of synchrony in different combinations of neurons and suggest that multiple information streams are conveyed concurrently by fluctuations in the synchrony of on-going activity.
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Baker, Stuart N., et George L. Gerstein. « Improvements to the Sensitivity of Gravitational Clustering for Multiple Neuron Recordings ». Neural Computation 12, no 11 (1 novembre 2000) : 2597–620. http://dx.doi.org/10.1162/089976600300014863.
Texte intégralRésumé :
We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.
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Roy, A., P. N. Steinmetz, S. S. Hsiao, K. O. Johnson et E. Niebur. « Synchrony : A Neural Correlate of Somatosensory Attention ». Journal of Neurophysiology 98, no 3 (septembre 2007) : 1645–61. http://dx.doi.org/10.1152/jn.00522.2006.
Texte intégralRésumé :
We investigated whether synchrony between neuronal spike trains is affected by the animal's attentional state. Cross-correlation functions between pairs of spike trains in the second somatosensory cortex (SII) of three macaque monkeys trained to switch attention between a visual task and a tactile task were computed. We previously showed that the majority of recorded neuron pairs (66%) in SII cortex fire synchronously while the animals performed either task and that in a subset of neuron pairs (17%), the degree of synchrony was affected by the animal's attentional state. Of the neuron pairs that showed changes in synchrony with attention, about 80% showed increased synchrony when the animal attended to the tactile stimulus. Here, we show that peak correlation typically occurred at a delay <25 ms; most commonly the delay was close to zero. Half-widths of the correlation peaks were distributed between a few milliseconds and hundreds of milliseconds, with the majority lying <100 ms and the mode of the distribution around 20–30 ms. Maximal change in synchrony occurred mainly during the periods when the stimulus was present, and synchrony usually increased when attention was on the tactile stimulus. If periods of elevated firing rates around the motor response times were removed from the analysis, the percentage of pairs that changed the degree of synchrony with attention more than doubled (from 35 to 72%). The observed effects did not depend on details of the statistical criteria or of the time window used in the analysis.
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Rodriguez-Falces, Javier, Francesco Negro et Dario Farina. « Correlation between discharge timings of pairs of motor units reveals the presence but not the proportion of common synaptic input to motor neurons ». Journal of Neurophysiology 117, no 4 (1 avril 2017) : 1749–60. http://dx.doi.org/10.1152/jn.00497.2016.
Texte intégralRésumé :
We investigated whether correlation measures derived from pairs of motor unit (MU) spike trains are reliable indicators of the degree of common synaptic input to motor neurons. Several 50-s isometric contractions of the biceps brachii muscle were performed at different target forces ranging from 10 to 30% of the maximal voluntary contraction relying on force feedback. Forty-eight pairs of MUs were examined at various force levels. Motor unit synchrony was assessed by cross-correlation analysis using three indexes: the output correlation as the peak of the cross-histogram (ρ) and the number of synchronous spikes per second (CIS) and per trigger (E). Individual analysis of MU pairs revealed that ρ, CIS, and E were most often positively associated with discharge rate (87, 85, and 76% of the MU pairs, respectively) and negatively with interspike interval variability (69, 65, and 62% of the MU pairs, respectively). Moreover, the behavior of synchronization indexes with discharge rate (and interspike interval variability) varied greatly among the MU pairs. These results were consistent with theoretical predictions, which showed that the output correlation between pairs of spike trains depends on the statistics of the input current and motor neuron intrinsic properties that differ for different motor neuron pairs. In conclusion, the synchronization between MU firing trains is necessarily caused by the (functional) common input to motor neurons, but it is not possible to infer the degree of shared common input to a pair of motor neurons on the basis of correlation measures of their output spike trains. NEW & NOTEWORTHY The strength of correlation between output spike trains is only poorly associated with the degree of common input to the population of motor neurons. The synchronization between motor unit firing trains is necessarily caused by the (functional) common input to motor neurons, but it is not possible to infer the degree of shared common input to a pair of motor neurons on the basis of correlation measures of their output spike trains.
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Harrison, Matthew T. « Accelerated Spike Resampling for Accurate Multiple Testing Controls ». Neural Computation 25, no 2 (février 2013) : 418–49. http://dx.doi.org/10.1162/neco_a_00399.
Texte intégralRésumé :
Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.
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Muiño, David Picado, et Christian Borgelt. « Frequent item set mining for sequential data : Synchrony in neuronal spike trains ». Intelligent Data Analysis 18, no 6 (29 octobre 2014) : 997–1012. http://dx.doi.org/10.3233/ida-140681.
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Baker, S. N., R. Spinks, A. Jackson et R. N. Lemon. « Synchronization in Monkey Motor Cortex During a Precision Grip Task. I. Task-Dependent Modulation in Single-Unit Synchrony ». Journal of Neurophysiology 85, no 2 (1 février 2001) : 869–85. http://dx.doi.org/10.1152/jn.2001.85.2.869.
Texte intégralRésumé :
Neural synchronization in the cortex, and its potential role in information coding, has attracted much recent attention. In this study, we have recorded long spike trains (mean, 33,000 spikes) simultaneously from multiple single neurons in the primary motor cortex (M1) of two conscious macaque monkeys performing a precision grip task. The task required the monkey to use its index finger and thumb to move two spring-loaded levers into a target, hold them there for 1 s, and release for a food reward. Synchrony was analyzed using a time-resolved cross-correlation method, normalized using an estimate of the instantaneous firing rate of the cell. This was shown to be more reliable than methods using trial-averaged firing rate. A total of 375 neurons was recorded from the M1 hand area; 235 were identified as pyramidal tract neurons. Synchrony was weak [mean k′ = 1.05 ± 0.04 (SD)] but widespread among pairs of M1 neurons (218/1359 pairs with above-chance synchrony), including output neurons. Synchrony usually took the form of a broad central peak [average width, 18.7 ± 8.7 (SD) ms]. There were marked changes during different phases of the task. As a population, synchrony was greatest during the steady hold period in striking contrast to the averaged cell firing rate, which was maximal when the animal was moving the levers into target. However, the modulation of synchrony during task performance showed considerable variation across individual cell pairs. Two types of synchrony were identified: oscillatory (with periodic side lobes in the cross-correlation) and nonoscillatory. Their relative contributions were quantified by filtering the cross-correlations to exclude either frequencies from 18 to 37 Hz or all higher and lower frequencies. At the peak of population synchrony during the hold period, about half (51.7% in one monkey, 56.2% in the other) of the synchronization was within this oscillatory bandwidth. This study provides strong support for assemblies of neurons being synchronized during specific phases of a complex task with potentially important consequences for both information processing within M1 and for the impact of M1 commands on target motoneurons.
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Mikula, Shawn, et Ernst Niebur. « The Effects of Input Rate and Synchrony on a Coincidence Detector : Analytical Solution ». Neural Computation 15, no 3 (1 mars 2003) : 539–47. http://dx.doi.org/10.1162/089976603321192068.
Texte intégralRésumé :
We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero correlation (independent processes) to complete correlation (identical processes).
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Kass, Robert E., Ryan C. Kelly et Wei-Liem Loh. « Assessment of synchrony in multiple neural spike trains using loglinear point process models ». Annals of Applied Statistics 5, no 2B (juin 2011) : 1262–92. http://dx.doi.org/10.1214/10-aoas429.
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Huang, Xin, et Stephen G. Lisberger. « Circuit mechanisms revealed by spike-timing correlations in macaque area MT ». Journal of Neurophysiology 109, no 3 (1 février 2013) : 851–66. http://dx.doi.org/10.1152/jn.00775.2012.
Texte intégralRésumé :
We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.
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Chang, E. Y., K. F. Morris, R. Shannon et B. G. Lindsey. « Repeated Sequences of Interspike Intervals in Baroresponsive Respiratory Related Neuronal Assemblies of the Cat Brain Stem ». Journal of Neurophysiology 84, no 3 (1 septembre 2000) : 1136–48. http://dx.doi.org/10.1152/jn.2000.84.3.1136.
Texte intégralRésumé :
Many neurons exhibit spontaneous activity in the absence of any specific experimental perturbation. Patterns of distributed synchrony embedded in such activity have been detected in the brain stem, suggesting that it represents more than “baseline” firing rates subject only to being regulated up or down. This work tested the hypothesis that nonrandom sequences of impulses recur in baroresponsive respiratory-related brain stem neurons that are elements of correlational neuronal assemblies. In 15 Dial-urethan anesthetized vagotomized adult cats, neuronal impulses were monitored with microelectrode arrays in the ventral respiratory group, nucleus tractus solitarius, and medullary raphe nuclei. Efferent phrenic nerve activity was recorded. Spike trains were analyzed with cycle-triggered histograms and tested for respiratory-modulated firing rates. Baroreceptors were stimulated by unilateral pressure changes in the carotid sinus or occlusion of the descending aorta; changes in firing rates were assessed with peristimulus time and cumulative sum histograms. Cross-correlation analysis was used to test for nonrandom temporal relationships between spike trains. Favored patterns of interspike interval sequences were detected in 31 of 58 single spike trains; 18 of the neurons with significant sequences also had short-time scale correlations with other simultaneously recorded cells. The number of distributed patterns exceeded that expected under the null hypothesis in 12 of 14 data sets composed of 4–11 simultaneously recorded spike trains. The data support the hypothesis that baroresponsive brain stem neurons operate in transiently configured coordinated assemblies and suggest that single neuron patterns may be fragments of distributed impulse sequences. The results further encourage the search for coding functions of spike patterns in the respiratory network.
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Kenyon, Garrett T., James Theiler, John S. George, Bryan J. Travis et David W. Marshak. « Correlated Firing Improves Stimulus Discrimination in a Retinal Model ». Neural Computation 16, no 11 (1 novembre 2004) : 2261–91. http://dx.doi.org/10.1162/0899766041941916.
Texte intégralRésumé :
Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.
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HANSEL, D. « SYNCHRONIZED CHAOS IN LOCAL CORTICAL CIRCUITS ». International Journal of Neural Systems 07, no 04 (septembre 1996) : 403–15. http://dx.doi.org/10.1142/s0129065796000385.
Texte intégralRésumé :
Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This is in marked contrast to the behavior of cortical neurons in-vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a “hypercolumn” in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin–Huxley type model with several cellular and synaptic conductances. The pattern of connectivity is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and analyzed. The relation between the results of the model and experiments in visual cortex is discussed.
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Roy, A., P. N. Steinmetz, K. O. Johnson et E. Niebur. « Model-free detection of synchrony in neuronal spike trains, with an application to primate somatosensory cortex ». Neurocomputing 32-33 (juin 2000) : 1103–8. http://dx.doi.org/10.1016/s0925-2312(00)00284-8.
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Lindsey, B. G., Y. M. Hernandez, K. F. Morris, R. Shannon et G. L. Gerstein. « Respiratory-related neural assemblies in the brain stem midline ». Journal of Neurophysiology 67, no 4 (1 avril 1992) : 905–22. http://dx.doi.org/10.1152/jn.1992.67.4.905.
Texte intégralRésumé :
1. The initial objective of this study was to determine whether respiratory-related neural assemblies exist in the brain stem midline. A second goal was to seek evidence for concurrent relationships among the neurons that could generate the detected synchrony. 2. Experiments were conducted on anesthetized, paralyzed, bilaterally vagotomized, artificially ventilated cats. Spike trains of four to nine simultaneously monitored neurons were recorded in the regions of n. raphe obscurus-n. raphe pallidus and n. raphe magnus. 3. Data were analyzed with cycle-triggered histograms, cross-correlograms, snowflakes, and the gravitational representation. A significance test for the gravity method was developed and tested with spike trains generated by simulated networks with defined connections. 4. Ninety-three groups of neurons from 24 cats were studied. Thirty-nine groups from 19 cats included neurons that discharged synchronously on a millisecond time scale; less than or equal to 19 pairs of synchronously discharging neurons were found in one group. Twenty-seven of these 39 groups included neurons that had respiratory-modulated firing rates and discharged synchronously with other group members. Synchronous assemblies included cells monitored at rostral or caudal locations, or both. 5. Six classes of relationships were inferred from groups of neurons with multiple correlations: divergence (n = 11); convergence (n = 7); connections with opposite actions between neurons (n = 5); projections of synchronous neurons to separate targets (n = 5); projections to one neuron in a synchronous group (n = 4); and projections between two synchronous groups with common elements (n = 6). 6. The results document the existence of assemblies of synchronously discharging respiratory-related neurons in midline regions of the brain stem and suggest that divergent excitatory and inhibitory connections within the midline participate in the generation of that synchrony. Links between assemblies may operate to stabilize their collective activity in a particular state.
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WRIGHT, J. J. « CORTICAL PHASE TRANSITIONS : PROPERTIES DEMONSTRATED IN CONTINUUM SIMULATIONS AT MESOSCOPIC AND MACROSCOPIC SCALES ». New Mathematics and Natural Computation 05, no 01 (mars 2009) : 159–83. http://dx.doi.org/10.1142/s1793005709001210.
Texte intégralRésumé :
Continuum simulations of cortical dynamics permit consistent simulations to be performed at different spatial scales, using scale-adjusted parameter values. Properties of the simulations described here accord with Freeman's experimental and theoretical findings on gamma synchrony, phase transition, phase cones, and null spikes. State equations include effects of retrograde action potential propagation into dendritic trees, and kinetics of AMPA, GABA, and NMDA receptors. Realistic field potentials and pulse rates, gamma resonance and oscillation, and 1/f2 background activity are obtained. Zero-lag synchrony and traveling waves occur as complementary aspects of cortical transmission, and lead/lag relations between excitatory and inhibitory cell populations vary systematically around transition to autonomous gamma oscillation. Autonomous gamma is initiated by focal excitation of excitatory cells and suppressed by laterally spreading trans-cortical excitation. By implication, patches of cortex excited to gamma oscillation can mutually synchronize into larger fields, self-organized into sequences by mutual negative feedback relations, while the sequence of synchronous fields is regulated both by cortical/subcortical interactions and by traveling waves in the cortex — the latter observable as phase cones. At a critical level of cortical excitation, just before transition to autonomous gamma, patches of cortex exhibit selective sensitivity to action potential pulse trains modulated in the gamma band, while autonomous gamma releases pulse trains modulated in the same band, implying coupling of input and output modes. Transition between input and output modes may be heralded by phase slips and null spikes. Synaptic segregation by retrograde action potential propagation implies state-specific synaptic information storage.