Effect of cross-trial nonstationarity on joint-spike events

Grün, Sonja; Riehle, Alexa; Diesmann, Markus

doi:10.1007/s00422-002-0386-2

Cited by 44 publications

(47 citation statements)

References 45 publications

(50 reference statements)

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“…The basic, and quite plausible, counterexamples are forms of trial-to-trial variability, such as latency variations (Ventura 2004) or slowly varying common input, such as that due to internal variables not controlled by the experiment (Baker and Gerstein 2001;Czanner et al 2008;Grün et al 2003;Kass and Ventura 2006). One proposal for dealing with such concerns is to incorporate trial-varying firing rates into parametric models of the spiking process, which can again be tested, for example, by estimated trial-varying firing rates incorporated into bootstrap tests (Bair et al 2001;Ben-Shaul et al 2001;Brody 1999;Pauluis and Baker 2000;Ventura et al 2005b).…”

Section: Summary and Discussionmentioning

confidence: 99%

Conditional modeling and the jitter method of spike resampling

Amarasingham

Harrison

Hatsopoulos

et al. 2012

Journal of Neurophysiology

122

169

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Amarasingham A, Harrison MT, Hatsopoulos NG, Geman S. Conditional modeling and the jitter method of spike resampling. J Neurophysiol 107: 517-531, 2012. First published October 26, 2011 doi:10.1152/jn.00633.2011.-The existence and role of fine-temporal structure in the spiking activity of central neurons is the subject of an enduring debate among physiologists. To a large extent, the problem is a statistical one: what inferences can be drawn from neurons monitored in the absence of full control over their presynaptic environments? In principle, properly crafted resampling methods can still produce statistically correct hypothesis tests. We focus on the approach to resampling known as jitter. We review a wide range of jitter techniques, illustrated by both simulation experiments and selected analyses of spike data from motor cortical neurons. We rely on an intuitive and rigorous statistical framework known as conditional modeling to reveal otherwise hidden assumptions and to support precise conclusions. Among other applications, we review statistical tests for exploring any proposed limit on the rate of change of spiking probabilities, exact tests for the significance of repeated fine-temporal patterns of spikes, and the construction of acceptance bands for testing any purported relationship between sensory or motor variables and synchrony or other fine-temporal events.

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Section: Summary and Discussionmentioning

confidence: 99%

Conditional modeling and the jitter method of spike resampling

Amarasingham

Harrison

Hatsopoulos

et al. 2012

Journal of Neurophysiology

122

169

View full text Add to dashboard Cite

show abstract

“…Within each window, the expected number of coincidences is calculated as the sum of the trial-by-trial expectancies each as the product of the marginal probabilities of firing (Grün et al, 2003). This procedure considers differences in firing rates across trials and therefore accounts for nonstationarities across trials.…”

Section: Discussionmentioning

confidence: 99%

“…First, coincidences of a certain temporal jitter are evaluated not by exclusive binning but by the "multiple shift" approach, which has a higher sensitivity (Grün et al, 1999). Second, the predicted number of coincident spikes is determined on a trial-by-trial basis, which takes into account nonstationarity of firing across trials (Grün et al, 2003). Still some criteria (described in Materials and Methods) have to be fulfilled by the datasets to enable reliable estimation of the significance of the synchrony.…”

Section: Detection Of Precise Spike Synchrony In Pairs Of Simultaneoumentioning

confidence: 99%

Long-Term Modifications in Motor Cortical Dynamics Induced by Intensive Practice

Kilavik¹,

Roux²,

Ponce‐Alvarez³

et al. 2009

J. Neurosci.

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The planning of goal-directed movements requires sensory, temporal, and contextual information to be combined. Sensorimotor functions are embedded in large neuronal networks, but it is unclear how networks organize their activity in space and time to optimize behavior. Temporal coordination of activity in many neurons within a network, e.g., spike synchrony, might be complementary to a firing rate code, allowing efficient computation with overall less population activity. Here we asked the question whether intensive practice induces long-term modifications in the temporal structure of synchrony and firing rate at the population level. Three monkeys were trained in a delayed pointing task in which the selection of movement direction depended on correct time estimation. The synchronous firing among pairs of simultaneously recorded neurons in motor cortex was analyzed using the "unitary event" technique. The evolution of synchrony in both time, within the trial, and temporal precision was then quantified at the level of an entire population of neurons by using two different quantification techniques and compared with the population firing rate. We find that the task timing was represented in the temporal structure of significant spike synchronization at the population level. During practice, the temporal structure of synchrony was shaped, with synchrony becoming stronger and more localized in time during late experimental sessions, in parallel with an improvement in behavioral performance. Concurrently, the average population firing rate mainly decreased. Performance optimization through practice might therefore be achieved by boosting the computational contribution of spike synchrony, allowing an overall reduction in population activity.

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“…No smoothing functions were applied to the CCs. Trial shuffling assumes stationarity across trials (Grün et al, 2003). We used autocorrelograms to distinguish between spike timing and latency or excitability covariations (Brody, 1999).…”

Section: Methodsmentioning

confidence: 99%

Differential Involvement of Excitatory and Inhibitory Neurons of Cat Motor Cortex in Coincident Spike Activity Related to Behavioral Context

Putrino

Brown²,

Mastaglia³

et al. 2010

Journal of Neuroscience

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To assess temporal associations in spike activity between pairs of neurons in the primary motor cortex (MI) related to different behaviors, we compared the incidence of coincident spiking activity of task-related (TR) and non-task-related (NTR) neurons during a skilled motor task and sitting quietly in adult cats (Felis domestica). Chronically implanted microwires were used to record spike activity of MI neurons in four animals (two male and two female) trained to perform a skilled reaching task or sit quietly. Neurons were identified as TR if spike activity was modulated during the task (and NTR if not). Based on spike characteristics, they were also classified as either regular-spiking (RS, putatively excitatory) or fast-spiking (FS, putatively inhibitory) neurons. Temporal associations in the activities of simultaneously recorded neurons were evaluated using shuffle-corrected cross-correlograms. Pairs of NTR and TR neurons showed associations in their firing patterns over wide areas of MI (representing forelimb and hindlimb movements) during quiet sitting, more commonly involving RS neurons. During skilled task performance, however, significantly coincident firing was seen almost exclusively between TR neurons in a smaller part of MI (representing forelimb movements), involving mainly FS neurons. The findings of this study show evidence for widespread interactions in MI when the animal sits quietly, which changes to a more specific and restricted pattern of interactions during task performance. Different populations of excitatory and inhibitory neurons appear to be synchronized during skilled movement and quiet sitting.

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Effect of cross-trial nonstationarity on joint-spike events

Cited by 44 publications

References 45 publications

Conditional modeling and the jitter method of spike resampling

Conditional modeling and the jitter method of spike resampling

Long-Term Modifications in Motor Cortical Dynamics Induced by Intensive Practice

Differential Involvement of Excitatory and Inhibitory Neurons of Cat Motor Cortex in Coincident Spike Activity Related to Behavioral Context

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