Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures

Abstract

Recurring patterns of neural activity, a potential substrate of both information transfer and transformation in cortical networks, have been observed in the intact brain and in brain slices. Do these patterns require the inherent cortical microcircuitry of such preparations or are they a general property of self-organizing neuronal networks? In networks of dissociated cortical neurons from rats--which lack evidence of the intact brain's intrinsic cortical architecture--we have observed a robust set of spontaneously repeating spatiotemporal patterns of neural activity, using a template-matching algorithm that has been successful both in vivo and in brain slices. The observed patterns in cultured monolayer networks are stable over minutes of extracellular recording, occur throughout the culture's development, and are temporally precise within milliseconds. The identification of these patterns in dissociated cultures opens a powerful methodological avenue for the study of such patterns, and their persistence despite the topological and morphological rearrangements of cellular dissociation is further evidence that precisely timed patterns are a universal emergent feature of self-organizing neuronal networks.

Fig. 1

Shuffling methods. (Top) Spike swapping preserves the dataset’s spike-timing distribution and electrode distribution. Note that swapping can be pair-wise (i.e., between two electrodes, as demonstrated with the two swaps to the left) or higher-order (as demonstrated with the three-wise swap to the right). (Bottom) Spike jittering preserves population modulations in firing rate and each electrode’s ISI distribution approximately, and the dataset’s electrode distribution exactly.

Fig. 2

Precisely timed sequences of neural activity repeat spontaneously in networks of dissociated cortical neurons. (A) Raster plot showing sequence repetitions. Each gray dot represents an action potential detected on a specific electrode. Sixty-two instances of a repeating four-spike sequence are traced in dark blue lines, seven instances of a five-spike sequence in orange, four instances of a four-spike sequence in green, and eleven instances of a three-spike sequence in red (fewer instances may be visible due to overlap when displayed at this resolution). Arrowheads indicate population bursts. In this panel, the ordering of electrodes is not related to MEA geometry, but was chosen to avoid the overlapping of sequence traces. (B) Action potential waveforms for the eleven repetitions of the red sequence in A. Each row shows the waveforms recorded from one electrode and each column is one sequence repetition. Electrode labels indicate the column, row location of the electrode on the MEA (see panel C). (C) Spatial propagation of each sequence shown in A. Each electrode of the MEA is represented by a light gray circle. The dark blue solid arrows show the propagation of the dark blue sequence from A. The red dot-dashed arrows represent the red sequence depicted in panels A and B. The orange dashed arrows represent the orange sequence depicted in panel A and the green dotted arrows represent the green sequence. Arrows indicate the sequence’s origin and direction of propagation. The empty space at column 1, row 5 is the approximate location of the ground electrode. (D) The time course of the four sequence families, using the same color coding as panels A–C and the same line style as C. Electrode ordering as in panel A. These patterns were detected with a template-matching algorithm using a window size T = 200 ms and a precision of 1 ms (see Methods).

Fig. 3

Properties of detected sequences. (A) Histogram of times between sequence repetitions from those sequences repeating three or more times (black bars). Sequences repeated with a mean interval of 13.7 ± 13.0 seconds, though many occur in close succession (left peak of histogram), due to the frequent occurrence of sequences in population bursts (see text). Additional peaks at 14 and 23 seconds are due to bursts in the most active culture studied, which contained three bursts with inter-burst intervals of 14.1 and 23 seconds. The histogram with this culture excluded is shown in gray. (B) Participation of each electrode in sequences, data from one representative culture. The total number of spikes detected on an electrode (x-axis) is plotted vs. the total number of spikes detected on the same electrode that take part in any sequence (y-axis). Each electrode is represented by one point. The slope of the best-fit line through these points can be used to estimate the percentage of spikes taking part in sequences on an electrode, 48% in this culture (R² = 0.93) (if every spike detected on an electrode participated in a sequence, the best-fit line would have a slope of one).

Fig. 4

Sequences repeat more frequently in actual data from cultures aged 21 DIV than in shuffled data. The template-matching algorithm was run at various precisions (1, 2, 5, 10, and 20 ms) and the number of detected sequences was compared to the number of sequences observed in shuffled versions of the same dataset. (A) The mean number of detected sequence families repeating 3 or more times in the actual data (solid black bars, ±SEM), along with the mean percentage of these sequences explained by shuffled data (spike-swapped white bars; jittered with 2 ms Gaussian kernel dark grey bars; jittered with 20 ms Gaussian kernel light grey bars). Percentages are calculated as the number of sequence families detected in shuffled data divided by the number detected in actual data, and the mean ±SEM of these ratios is plotted (light gray boxes next to black bars of unshuffled data) with the ordinate, ranging from 0–100%, scaled to the number of sequences detected in the actual data. Tick marks are at 20% intervals for these minor axes. Significant differences are indicated by asterisks. (B) Average length (±SD) of sequences repeating three or more times at each precision. The similarity between lengths indicates that our choice of precision does not affect the average length of detected sequences.

Fig. 5

Sequences repeat more frequently in actual data than in shuffled data at 35 DIV. This figure mirrors Figure 4, but uses 11 cultures derived from 3 separate platings, aged 35 DIV, instead of 21 DIV. The observation of significant precisely timed sequences at both stages of development argues against any developmental transience of this phenomenon.

Fig. 6

Persistence of detected sequences. The three most frequently recurring sequences were sought in actual data during the ten minutes following the 16^th minute. The number of sequences observed in the actual data was compared to the number observed in 20 shuffled versions of the same data, using the most stringent shuffling method (i.e., spike jittering with a 2 ms Gaussian kernel). When searching the shuffled datasets, the three most frequently recurring sequences from the shuffled data were sought. If the actual data had more observed sequences than any of the 20 shuffles, it was considered to contain significantly more of the three most frequently occurring sequences than shuffled data (P < 0.05). The percentage of cultures passing this test is shown for both 21 DIV (black bars) and 35 DIV datasets (white bars) for each minute. Most or all of the cultures (11 of 12 at 21 DIV, 12 of 12 at 35 DIV) passed this test at minute zero, even though the method for tracking persistence does not discard matched spikes, thus providing a control for the template-matching algorithm. These results suggest that the most frequently recurring patterns are stable over several minutes following their initial observation.

Fig. 7

The distribution of sequence sizes obeys a power law probability distribution. When the number of electrodes taking part in each detected sequence is graphed against its probability of occurrence, the distribution can be fit by a power law (red dashed line), P(n) ~ n^{− α}, where n is the event size, P(n) is its normalized frequency of occurrence in our datasets, and α is the power law’s exponent. On log-log plots, such as these, graphed power laws appear linear, with slope α. For the observed sequences in the actual data α = −3.1 ± 0.2 (±95% CI, R² = 0.97; A). However, we find similar scale invariance in our spike-swapped (α = −3.2 ± 0.2, R² = 0.97; B) and spike-jittered data (α = −3.3 ± 0.2, R² = 0.97; C), minimizing the importance of scale invariance in explaining our significantly repeating patterns.