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Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations

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Abstract The onset of a sensory stimulus elicits transient bursts of activity in neural populations, which are presumed to convey information about the stimulus to downstream populations. The time at which these synchronized bursts reach their peak is highly variable across stimulus presentations, but the relative timing of bursts across interconnected brain regions may be less variable, especially for regions that are strongly functionally coupled. We developed a simple analytical framework that obtains good estimates of population burst times on a trial-by-trial basis, and of the correlations in the timing of evoked population bursts across areas. We show that this method performs well on simulated data, and is 85 to 90% faster than an alternative, recently-published method while also being much easier to apply. Using this new approach, we examined the relative timing of the first two population bursts following the onset of a drifting grating stimulus in large-scale recordings of spiking activity from six cortical visual areas and one visual thalamic nucleus in thirteen mice. The new method allowed us to identify mouse-to-mouse variation in peak times and region-to-region functional coupling. While all results were consistent with known anatomy and physiology, we found some sequences of activity across areas to be the same across all mice, while others varied with the individual. The general approach can thus produce sensitive analyses of timing relationships across neural populations. Significant Statement Careful analysis can reveal strong and precisely-timed interactions across multiple brain areas from small populations of spiking neurons. We developed a computationally efficient procedure that allowed us to examine the relative timing and coupling of 7 visual areas (6 cortical and one thalamic) and compare results in over 10 mice. The method can be used to track the flow of information across the brain in response to stimuli or during a behavioral task.
Title: Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations
Description:
Abstract The onset of a sensory stimulus elicits transient bursts of activity in neural populations, which are presumed to convey information about the stimulus to downstream populations.
The time at which these synchronized bursts reach their peak is highly variable across stimulus presentations, but the relative timing of bursts across interconnected brain regions may be less variable, especially for regions that are strongly functionally coupled.
We developed a simple analytical framework that obtains good estimates of population burst times on a trial-by-trial basis, and of the correlations in the timing of evoked population bursts across areas.
We show that this method performs well on simulated data, and is 85 to 90% faster than an alternative, recently-published method while also being much easier to apply.
Using this new approach, we examined the relative timing of the first two population bursts following the onset of a drifting grating stimulus in large-scale recordings of spiking activity from six cortical visual areas and one visual thalamic nucleus in thirteen mice.
The new method allowed us to identify mouse-to-mouse variation in peak times and region-to-region functional coupling.
While all results were consistent with known anatomy and physiology, we found some sequences of activity across areas to be the same across all mice, while others varied with the individual.
The general approach can thus produce sensitive analyses of timing relationships across neural populations.
Significant Statement Careful analysis can reveal strong and precisely-timed interactions across multiple brain areas from small populations of spiking neurons.
We developed a computationally efficient procedure that allowed us to examine the relative timing and coupling of 7 visual areas (6 cortical and one thalamic) and compare results in over 10 mice.
The method can be used to track the flow of information across the brain in response to stimuli or during a behavioral task.

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