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Trialtotrial variability and its effect on timevarying dependence between two neurons
 J. Neurophysiology
, 2005
"... The joint peristimulus time histogram (JPSTH) and crosscorrelogram 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 (Cai et al. 2004a) we showed how a Bootstrap evaluation of the peaks in ..."
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Cited by 23 (8 self)
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The joint peristimulus time histogram (JPSTH) and crosscorrelogram 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 (Cai et al. 2004a) 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 timevarying correlation. As noted by Brody (1999a,b) and BenShaul et al. (2001), trialtotrial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a method of estimating the timedependent trialtotrial variation in spike trains that may exceed the natural variation displayed by Poisson and nonPoisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firingrate function that fluctuates over time. The method developed here uses principal components of the trialtotrial variability in firing rate functions to obtain a small number of parameters (typically two or three) that characterize the deviation of each trial’s firing rate function from the acrosstrial average firing rate, represented by the
Conditional modeling and the jitter method of spike resampling
 Journal of Neurophysiology (2011), Published online
"... Spike Resampling, ” [14] and provides further details, comments, references, and equations that were omitted from this main text in the interest of brevity. To ease referencing, the sectioning of the report follows that of the main text. A few of our remarks in the supplement may be of interest to ..."
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Cited by 6 (1 self)
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Spike Resampling, ” [14] and provides further details, comments, references, and equations that were omitted from this main text in the interest of brevity. To ease referencing, the sectioning of the report follows that of the main text. A few of our remarks in the supplement may be of interest to a broad audience. For quick identification, these highlevel remarks are bordered by a left vertical bar, like the one to the left of this paragraph. The bulk of this document, however, contains technical details about the various simulations and data analyses presented in the main text. These would primarily be of interest to a reader who was hoping to reproduce our methods exactly. There is also a selfcontained Mathematical Appendix at the end of the supplement that provides a more formal treatment of jitter. 1
Geman: A rate and historypreserving resampling algorithm for neural spike trains. Neural Comput 2009
, 1998
"... Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications it is desirable to have resamples that preserve certain nonPoisson properties, like refractory periods and bursting, and that are also robust to trialtotrial variability. “Pat ..."
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Cited by 5 (1 self)
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Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications it is desirable to have resamples that preserve certain nonPoisson properties, like refractory periods and bursting, and that are also robust to trialtotrial variability. “Pattern jitter ” is a resampling technique that accomplishes this by preserving the recent spiking history of all spikes and constraining resampled spikes to remain close to their original positions. The resampled spike times are maximally random up to these constraints. Dynamic programming is used to create an efficient resampling algorithm. 1
Statistical Identification of Synchronous Spiking
"... 3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7 ..."
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Cited by 4 (3 self)
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3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7
A Framework for Evaluating Pairwise and Multiway Synchrony Among StimulusDriven Neurons
"... Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for timevarying firing rates that typically appear in stimulusdriven (or ac ..."
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Cited by 4 (2 self)
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Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for timevarying firing rates that typically appear in stimulusdriven (or actiondriven) neurons, nor do they incorporate nonPoisson history effects or covariate effects. We generalize the usual approach, combining point process regression models of individualneuron activity with loglinear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then go on to assess the amount of data needed to reliably detect multiway spiking.
Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events
, 2013
"... The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether ..."
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Cited by 3 (1 self)
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The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent nonPoisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a lognormal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate
Inferring spiketimingdependent plasticity from spike train data
"... Synaptic plasticity underlies learning and is thus central for development, memory, and recovery from injury. However, it is often difficult to detect changes in synaptic strength in vivo, since intracellular recordings are experimentally challenging. Here we present two methods aimed at inferring c ..."
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Cited by 1 (0 self)
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Synaptic plasticity underlies learning and is thus central for development, memory, and recovery from injury. However, it is often difficult to detect changes in synaptic strength in vivo, since intracellular recordings are experimentally challenging. Here we present two methods aimed at inferring changes in the coupling between pairs of neurons from extracellularly recorded spike trains. First, using a generalized bilinear model with Poisson output we estimate timevarying coupling assuming that all changes are spiketimingdependent. This approach allows modelbased estimation of STDP modification functions from pairs of spike trains. Then, using recursive pointprocess adaptive filtering methods we estimate more general variation in coupling strength over time. Using simulations of neurons undergoing spiketiming dependent modification, we show that the true modification function can be recovered. Using multielectrode data from motor cortex we then illustrate the use of this technique on in vivo data. 1
Innovative Methodology Hierarchical Bayesian Modeling and Markov Chain Monte Carlo Sampling for TuningCurve Analysis
, 2009
"... You might find this additional information useful... Supplemental material for this article can be found at: ..."
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You might find this additional information useful... Supplemental material for this article can be found at:
reactivation
"... A binless correlation measure reduces the variability of memory ..."
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