Data Analysis Clausole campione

Data Analysis. Once that single unit and multi-unit activity was found and behavioural scoring was done, we utilized these data to create four matrices on MatLab for each experimental session. First, we compared various single unit’s features between the two conditions: we analysed three standard measures - the burst index, the ISI (InterSpike Interval)’s coefficient of variation and the position of ▇▇▇’▇ maximum (▇▇▇▇▇▇▇▇▇▇▇▇▇▇ and Goldman-Rakic, 2002). In addition, we also compared the mean firing rate, the variability of the firing rate and the peak of the firing rate. These three measures were computed in a similar way as the successive analysis: for every unit, the whole session was binned in 200ms windows with a step of 20ms (Sliding Windows procedure) and the mean firing rate for each window was calculated. The mean firing rate of every neuron was the mean of the mean firing rate in each window; the variability of the firing rate was operationalized as the standard deviation of the mean firing rates for every window and the peak of the firing rate was the maximus of mean firing rate in a 200ms window.