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. 2019 Oct 25;6:231. doi: 10.1038/s41597-019-0242-z

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 8 — Spike – spike-LFP functions. (a) (Top) Example raster plot for 96 trials of a single condition for the first neuron picked up on electrode labeled ‘AD01’. A single condition was repeated 96 times instead of 4 for the purpose of this raster plot. (a) (Bottom) PSTH from all trials shows maximum firing ~175 ms after the stimulus onset for this neuron with 95% confidence intervals (green shade around average trace). Firing has been binned into 10 ms segments. The vertical red line indicates interactive temporal alignment between the two plots at the maximum firing rate for the selected neuron, and the purple shade the period of the stimulus presentation. (b) Tuning curves function. (LEFT): Users select the neurons and sequentially the conditions (and their order) that would formulate the x-axis (right side of the conditions selection window) for the tuning curve. Additionally, we included a selection for the time-window where the spikes would be counted. (RIGHT): Tuning curve for an example neuron, selected from the window on the left side of Figure b. The green shade indicates the 95% confidence intervals. The x-axis shows the different experimental conditions at the order selected on the previous window. This neuron expresses selectivity for the condition “Stim On −1/4 pi”. (c) Noise correlation. The function selects all the neurons that elicited spikes within the trials imported and displays a nxn figure where the noise correlation is computed for all combinations of neurons. Specifically for the dataset illustrated, there were 53 unique neurons picked up by the electrodes (according to the spike sorting step). This figure shows the computation of noise-correlation on all trials for the presentation 96 trials of a motion stimulus, and spikes are selected on [0,300] ms around the stimuli presentations. (d) Spike field coherence for an example neuron picked up from the 7th electrode (AD07) for all trials of the motion stimulus condition “Stim On 0”. The spike-field coherence window displays spectral influence of a single neuron to all 32 electrodes. Frequency is shown up to 50 Hz. Time selection around each spike was [−150, 150] ms. (e) Spike triggered average of a neuron picked up on electrode labeled AD01 with 95% confidence intervals for each electrode’s average trace (gray shade). A graph of the linear probe with the relative electrode locations is displayed on the left of the figure. The time selection around the spikes was set to [−150, 150] ms for all trials of all experimental conditions. All traces have been aligned to the same time-selection (0 ms – time occurrence of the spikes of AD01).