Specialization and hierarchy are organizing principles for primate cortex yet there

Specialization and hierarchy are organizing principles for primate cortex yet there is little direct evidence for how cortical areas are specialized in the temporal domain. exists for cognitive abstraction within prefrontal cortex2. In the temporal domain higher areas can activate selectively for stimuli that are coherent over longer periods of time3 4 It remains an open question whether temporal specialization arises from a cortical area’s intrinsic dynamical properties that is related to dynamics that exist even in the absence of direct stimulus processing. We hypothesized that differential dynamics would be manifested in the timescales of fluctuations in single-neuron spiking activity. Variable neuronal activity is ubiquitous across the cortex5 6 yet it has been unclear what the timescales PIK-75 underlying this variability are or whether these timescales differ across areas. Neuronal activity fluctuates over a wide range of timescales with potential contributions from distinct underlying mechanisms. For example the timescales of correlated fluctuations of activity within a local microcircuit are likely longer than the timescales timescales of single-neuron NP burstiness and refractoriness7 but shorter than the timescales of drifts in arousal. In typical electrophysiological recordings PIK-75 from behaving animals spike trains from a single neuron are recorded over many trials of a task. Using single-neuron spike trains we sought to characterize these underlying fluctuations in activity that are not locked to trial onset. To measure the timescales of these fluctuations we used the spike-count autocorrelation for pairs of time bins separated by a time lag. The spike-count autocorrelation is calculated as the correlation coefficient between the PIK-75 number of spikes in each time bin across all trials (Online Methods). As the time lag increases the autocorrelation decays according to the fluctuation timescales8 (Supplementary Mathematical Note). We measured intrinsic timescales using single-neuron spike trains in datasets from six research groups recorded PIK-75 in a total of 26 monkeys that include seven cortical areas (Fig. 1a). Five cortical areas are constituents of the visual-prefrontal hierarchy including sensory parietal association and prefrontal cortex: medial-temporal area (MT) in visual cortex; lateral intraparietal area (LIP) in parietal association cortex; lateral prefrontal cortex (LPFC); orbitofrontal cortex (OFC); and anterior cingulate cortex (ACC). To test for generality of results outside of the visual system we also examined two somatosensory areas: primary somatosensory cortex (S1) and secondary somatosensory cortex (S2). These areas span multiple levels of the anatomical hierarchy defined by the laminar patterns of long-range projections among cortical areas9 10 (Fig. 1b). For each dataset monkeys were engaged in cognitive tasks. We restricted our analysis to one epoch of the task the foreperiod that begins each trial. During the foreperiod the monkey was in a controlled attentive state awaiting stimulus onset (fixation of eye position for visual tasks lever hold for the somatosensory PIK-75 task). This restriction minimizes stimulus-related confounds and allows application of the same analyses across areas and datasets. This definition of intrinsic timescale does not refer to single-neuron physiology or imply that the timescale does not change with stimulus conditions. Figure 1 Spike-count autocorrelation reveals a hierarchical ordering of intrinsic timescales. (a) Datasets span seven cortical areas in the macaque monkey: MT LIP LPFC OFC ACC S1 and S2. (b) Anatomical hierarchy of the areas based on long-range projection … The decay of autocorrelation with increasing time lag could be well fit by an exponential decay with an offset (Fig. 1c). This fit was obtained at the population level rather than single neuron level (Online Methods and Supplementary Figs. 1 & 2) enabling us to extract an intrinsic timescale as a population-level statistic for each area in a dataset. Within each dataset the intrinsic timescales differed across areas in the range of 50-350 ms. Over all datasets we found PIK-75 a consistent ordering of the intrinsic timescales across cortical areas (< 10?5 = 0.89 Spearman's rank correlation) (Fig. 1d). Sensory cortex showed shorter timescales.